Automated data scaling, alignment, and organizing based on predefined parameters within surgical networks

ABSTRACT

A system for automatically fusing data from a medical procedure is disclosed. The system includes a medical hub comprising at least one processor and at least one memory. The one processor is configured to access a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period, access a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period, scale the second dataset to match the first data sampling rate, fuse the first dataset and the second dataset into a composite dataset, align the first dataset and the second dataset in the composite dataset, cause display of the composite dataset, generate a graphical overlay on top of the display of the composite dataset, and transmit the composite dataset to a remote server.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 62/729,177, titled AUTOMATED DATA SCALING, ALIGNMENT, AND ORGANIZING BASED ON PREDEFINED PARAMETERS WITHIN A SURGICAL NETWORK BEFORE TRANSMISSION, filed on Sep. 10, 2018, the disclosure of which is herein incorporated by reference in its entirety.

The present application also claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 62/692,747, titled SMART ACTIVATION OF AN ENERGY DEVICE BY ANOTHER DEVICE, filed on Jun. 30, 2018, to U.S. Provisional Patent Application No. 62/692,748, titled SMART ENERGY ARCHITECTURE, filed on Jun. 30, 2018, and to U.S. Provisional Patent Application No. 62/692,768, titled SMART ENERGY DEVICES, filed on Jun. 30, 2018, the disclosure of each of which is herein incorporated by reference in its entirety.

The present application also claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 62/659,900, titled METHOD OF HUB COMMUNICATION, filed on Apr. 19, 2018, the disclosure of each of which is herein incorporated by reference in its entirety.

The present application also claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 62/650,898 filed on Mar. 30, 2018, titled CAPACITIVE COUPLED RETURN PATH PAD WITH SEPARABLE ARRAY ELEMENTS, to U.S. Provisional Patent Application Ser. No. 62/650,887, titled SURGICAL SYSTEMS WITH OPTIMIZED SENSING CAPABILITIES, filed Mar. 30, 2018, to U.S. Provisional Patent Application Ser. No. 62/650,882, titled SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL PLATFORM, filed Mar. 30, 2018, and to U.S. Provisional Patent Application Ser. No. 62/650,877, titled SURGICAL SMOKE EVACUATION SENSING AND CONTROLS, filed Mar. 30, 2018, the disclosure of each of which is herein incorporated by reference in its entirety.

The present application also claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No. 62/640,417, titled TEMPERATURE CONTROL IN ULTRASONIC DEVICE AND CONTROL SYSTEM THEREFOR, filed Mar. 8, 2018, and to U.S. Provisional Patent Application Ser. No. 62/640,415, titled ESTIMATING STATE OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR, filed Mar. 8, 2018, the disclosure of each of which is herein incorporated by reference in its entirety.

The present application also claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, to U.S. Provisional Patent Application Ser. No. 62/611,340, titled CLOUD-BASED MEDICAL ANALYTICS, filed Dec. 28, 2017, and to U.S. Provisional Patent Application Ser. No. 62/611,339, titled ROBOT ASSISTED SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of each of which is herein incorporated by reference in its entirety.

BACKGROUND

The present disclosure relates to various surgical systems. Surgical procedures are typically performed in surgical operating theaters or rooms in a healthcare facility such as, for example, a hospital. A sterile field is typically created around the patient. The sterile field may include the scrubbed team members, who are properly attired, and all furniture and fixtures in the area. Various surgical devices and systems are utilized in performance of a surgical procedure.

Furthermore, in the Digital and Information Age, medical systems and facilities are often slower to implement systems or procedures utilizing newer and improved technologies due to patient safety and a general desire for maintaining traditional practices. However, often times medical systems and facilities may lack communication and shared knowledge with other neighboring or similarly situated facilities as a result. To improve patient practices, it would be desirable to find ways to help interconnect medical systems and facilities better.

SUMMARY

In various embodiments, a system for automatically fusing data from a medical procedure is disclosed that includes a medical hub including at least one processor and at least one memory, and a remote server communicatively coupled to the medical hub. The at least one processor is configured to access a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period, access a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period, scale the second dataset to match the first data sampling rate, fuse the first dataset and the second dataset into a composite dataset, align the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded, cause display of the composite dataset, generate a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset, and transmit the composite dataset to the remote server.

In various embodiments, a method of a system for automatically fusing data from a medical procedure is disclosed, the system including a medical hub comprising at least one processor and at least one memory. The method includes accessing a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period, accessing a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period, scaling the second dataset to match the first data sampling rate, fusing the first dataset and the second dataset into a composite dataset, aligning the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded, causing display of the composite dataset, generating a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset, and transmitting the composite dataset to a remote server.

In various embodiments, a computer readable medium including no transitory signals and including instructions that, when executed by a processor, cause the processor to perform operations is disclosed. The operations include accessing a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period, accessing a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period, scaling the second dataset to match the first data sampling rate, fusing the first dataset and the second dataset into a composite dataset, aligning the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded, causing display of the composite dataset, generating a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset, and transmitting the composite dataset to a remote server.

FIGURES

The various aspects described herein, both as to organization and methods of operation, together with further objects and advantages thereof, may best be understood by reference to the following description, taken in conjunction with the accompanying drawings as follows.

FIG. 1 is a block diagram of a computer-implemented interactive surgical system, in accordance with at least one aspect of the present disclosure.

FIG. 2 is a surgical system being used to perform a surgical procedure in an operating room, in accordance with at least one aspect of the present disclosure.

FIG. 3 is a surgical hub paired with a visualization system, a robotic system, and an intelligent instrument, in accordance with at least one aspect of the present disclosure.

FIG. 4 is a partial perspective view of a surgical hub enclosure, and of a combo generator module slidably receivable in a drawer of the surgical hub enclosure, in accordance with at least one aspect of the present disclosure.

FIG. 5 is a perspective view of a combo generator module with bipolar, ultrasonic, and monopolar contacts and a smoke evacuation component, in accordance with at least one aspect of the present disclosure.

FIG. 6 illustrates individual power bus attachments for a plurality of lateral docking ports of a lateral modular housing configured to receive a plurality of modules, in accordance with at least one aspect of the present disclosure.

FIG. 7 illustrates a vertical modular housing configured to receive a plurality of modules, in accordance with at least one aspect of the present disclosure.

FIG. 8 illustrates a surgical data network comprising a modular communication hub configured to connect modular devices located in one or more operating theaters of a healthcare facility, or any room in a healthcare facility specially equipped for surgical operations, to the cloud, in accordance with at least one aspect of the present disclosure.

FIG. 9 illustrates a computer-implemented interactive surgical system, in accordance with at least one aspect of the present disclosure.

FIG. 10 illustrates a surgical hub comprising a plurality of modules coupled to the modular control tower, in accordance with at least one aspect of the present disclosure.

FIG. 11 illustrates one aspect of a Universal Serial Bus (USB) network hub device, in accordance with at least one aspect of the present disclosure.

FIG. 12 is a block diagram of a cloud computing system comprising a plurality of smart surgical instruments coupled to surgical hubs that may connect to the cloud component of the cloud computing system, in accordance with at least one aspect of the present disclosure.

FIG. 13 is a functional module architecture of a cloud computing system, in accordance with at least one aspect of the present disclosure.

FIG. 14 illustrates a diagram of a situationally aware surgical system, in accordance with at least one aspect of the present disclosure.

FIG. 15 is a timeline depicting situational awareness of a surgical hub, in accordance with at least one aspect of the present disclosure.

FIG. 16 is a block diagram of a system for automated scaling, organizing, fusing and aligning of disparate data sets, in accordance with at least one aspect of the present disclosure.

FIG. 17 is a set of graphs including a first graph depicting measured blood pressure verse time, a second graph depicting fused blood pressure verse time, and a third graph depicting blood pressure verse time for different sample rates, in accordance with at least one aspect of the present disclosure.

FIG. 18 is a graph depicting blood pressure relative to high and low thresholds, in accordance with at least one aspect of the present disclosure.

FIG. 19 is a graph depicting ultrasonic system frequency verse time, in accordance with at least one aspect of the present disclosure.

FIG. 20 is a graph depicting expected blood pressure for different vessel types, in accordance with at least one aspect of the present disclosure.

FIG. 21 is a block diagram depicting layered contextual information, in accordance with at least one aspect of the present disclosure.

FIG. 22 is a block diagram depicting instrument functional settings, in accordance with at least one aspect of the present disclosure.

FIG. 23 is a graph depicting force to fire (FTF) and firing velocity for patients having different complication risks.

DESCRIPTION

Applicant of the present application owns the following U.S. patent applications, filed on Nov. 6, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. patent application Ser. No. 16/182,224, titled SURGICAL         NETWORK, INSTRUMENT, AND CLOUD RESPONSES BASED ON VALIDATION OF         RECEIVED DATASET AND AUTHENTICATION OF ITS SOURCE AND INTEGRITY,         now U.S. Patent Application Publication No. 2019/0205441;     -   U.S. patent application Ser. No. 16/182,230, titled SURGICAL         SYSTEM FOR PRESENTING INFORMATION INTERPRETED FROM EXTERNAL         DATA, now U.S. Patent Application Publication No. 2019/0200980;     -   U.S. patent application Ser. No. 16/182,233, titled MODIFICATION         OF SURGICAL SYSTEMS CONTROL PROGRAMS BASED ON MACHINE LEARNING,         now U.S. Patent Application Publication No. 2019/0201123;     -   U.S. patent application Ser. No. 16/182,239, titled ADJUSTMENT         OF DEVICE CONTROL PROGRAMS BASED ON STRATIFIED CONTEXTUAL DATA         IN ADDITION TO THE DATA, now U.S. Patent Application Publication         No. 2019/0201124;     -   U.S. patent application Ser. No. 16/182,243, titled SURGICAL HUB         AND MODULAR DEVICE RESPONSE ADJUSTMENT BASED ON SITUATIONAL         AWARENESS, now U.S. Patent Application Publication No.         2019/0206542;     -   U.S. patent application Ser. No. 16/182,248, titled DETECTION         AND ESCALATION OF SECURITY RESPONSES OF SURGICAL INSTRUMENTS TO         INCREASING SEVERITY THREATS, now U.S. Pat. No. 10,943,454;     -   U.S. patent application Ser. No. 16/182,251, titled INTERACTIVE         SURGICAL SYSTEM, now U.S. Patent Application Publication No.         2019/0201125;     -   U.S. patent application Ser. No. 16/182,267, titled SENSING THE         PATIENT POSITION AND CONTACT UTILIZING THE MONO-POLAR RETURN PAD         ELECTRODE TO PROVIDE SITUATIONAL AWARENESS TO A SURGICAL         NETWORK, now U.S. Patent Application Publication No.         2019/0201128;     -   U.S. patent application Ser. No. 16/182,249, titled POWERED         SURGICAL TOOL WITH PREDEFINED ADJUSTABLE CONTROL ALGORITHM FOR         CONTROLLING END EFFECTOR PARAMETER, now U.S. Patent Application         Publication No. 2019/0201081;     -   U.S. patent application Ser. No. 16/182,246, titled ADJUSTMENTS         BASED ON AIRBORNE PARTICLE PROPERTIES, now U.S. Patent         Application Publication No. 2019/0204201;     -   U.S. patent application Ser. No. 16/182,256, titled ADJUSTMENT         OF A SURGICAL DEVICE FUNCTION BASED ON SITUATIONAL AWARENESS,         now U.S. Patent Application Publication No. 2019/0201127;     -   U.S. patent application Ser. No. 16/182,242, titled REAL-TIME         ANALYSIS OF COMPREHENSIVE COST OF ALL INSTRUMENTATION USED IN         SURGERY UTILIZING DATA FLUIDITY TO TRACK INSTRUMENTS THROUGH         STOCKING AND IN-HOUSE PROCESSES, now U.S. Patent Application         Publication No. 2019/0206556;     -   U.S. patent application Ser. No. 16/182,255, titled USAGE AND         TECHNIQUE ANALYSIS OF SURGEON/STAFF PERFORMANCE AGAINST A         BASELINE TO OPTIMIZE DEVICE UTILIZATION AND PERFORMANCE FOR BOTH         CURRENT AND FUTURE PROCEDURES, now U.S. Patent Application         Publication No. 2019/0201126;     -   U.S. patent application Ser. No. 16/182,269, titled IMAGE         CAPTURING OF THE AREAS OUTSIDE THE ABDOMEN TO IMPROVE PLACEMENT         AND CONTROL OF A SURGICAL DEVICE IN USE, now U.S. Patent         Application Publication No. 2019/0201129;     -   U.S. patent application Ser. No. 16/182,278, titled         COMMUNICATION OF DATA WHERE A SURGICAL NETWORK IS USING CONTEXT         OF THE DATA AND REQUIREMENTS OF A RECEIVING SYSTEM/USER TO         INFLUENCE INCLUSION OR LINKAGE OF DATA AND METADATA TO ESTABLISH         CONTINUITY, now U.S. Patent Application Publication No.         2019/0201130;     -   U.S. patent application Ser. No. 16/182,290, titled SURGICAL         NETWORK RECOMMENDATIONS FROM REAL TIME ANALYSIS OF PROCEDURE         VARIABLES AGAINST A BASELINE HIGHLIGHTING DIFFERENCES FROM THE         OPTIMAL SOLUTION, now U.S. Patent Application Publication No.         2019/0201102;     -   U.S. patent application Ser. No. 16/182,232, titled CONTROL OF A         SURGICAL SYSTEM THROUGH A SURGICAL BARRIER, now U.S. Patent         Application Publication No. 2019/0201158;     -   U.S. patent application Ser. No. 16/182,227, titled SURGICAL         NETWORK DETERMINATION OF PRIORITIZATION OF COMMUNICATION,         INTERACTION, OR PROCESSING BASED ON SYSTEM OR DEVICE NEEDS, now         U.S. Pat. No. 10,892,995;     -   U.S. patent application Ser. No. 16/182,231, titled WIRELESS         PAIRING OF A SURGICAL DEVICE WITH ANOTHER DEVICE WITHIN A         STERILE SURGICAL FIELD BASED ON THE USAGE AND SITUATIONAL         AWARENESS OF DEVICES, now U.S. Pat. No. 10,758,310;     -   U.S. patent application Ser. No. 16/182,229, titled ADJUSTMENT         OF STAPLE HEIGHT OF AT LEAST ONE ROW OF STAPLES BASED ON THE         SENSED TISSUE THICKNESS OR FORCE IN CLOSING, now U.S. Patent         Application Publication No. 2019/0200996;     -   U.S. patent application Ser. No. 16/182,234, titled STAPLING         DEVICE WITH BOTH COMPULSORY AND DISCRETIONARY LOCKOUTS BASED ON         SENSED PARAMETERS, now U.S. Patent Application Publication No.         2019/0200997;     -   U.S. patent application Ser. No. 16/182,240, titled POWERED         STAPLING DEVICE CONFIGURED TO ADJUST FORCE, ADVANCEMENT SPEED,         AND OVERALL STROKE OF CUTTING MEMBER BASED ON SENSED PARAMETER         OF FIRING OR CLAMPING, now U.S. Patent Application Publication         No. 2019/0201034;     -   U.S. patent application Ser. No. 16/182,235, titled VARIATION OF         RADIO FREQUENCY AND ULTRASONIC POWER LEVEL IN COOPERATION WITH         VARYING CLAMP ARM PRESSURE TO ACHIEVE PREDEFINED HEAT FLUX OR         POWER APPLIED TO TISSUE, now U.S. Patent Application Publication         No. 2019/0201044; and     -   U.S. patent application Ser. No. 16/182,238, titled ULTRASONIC         ENERGY DEVICE WHICH VARIES PRESSURE APPLIED BY CLAMP ARM TO         PROVIDE THRESHOLD CONTROL PRESSURE AT A CUT PROGRESSION         LOCATION, now U.S. Patent Application Publication No.         2019/0201080.

Applicant of the present application owns the following U.S. patent applications, filed on Sep. 10, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application No. 62/729,183, titled A         CONTROL FOR A SURGICAL NETWORK OR SURGICAL NETWORK CONNECTED         DEVICE THAT ADJUSTS ITS FUNCTION BASED ON A SENSED SITUATION OR         USAGE;     -   U.S. Provisional Patent Application No. 62/729,177, titled         AUTOMATED DATA SCALING, ALIGNMENT, AND ORGANIZING BASED ON         PREDEFINED PARAMETERS WITHIN A SURGICAL NETWORK BEFORE         TRANSMISSION;     -   U.S. Provisional Patent Application No. 62/729,176, titled         INDIRECT COMMAND AND CONTROL OF A FIRST OPERATING ROOM SYSTEM         THROUGH THE USE OF A SECOND OPERATING ROOM SYSTEM WITHIN A         STERILE FIELD WHERE THE SECOND OPERATING ROOM SYSTEM HAS PRIMARY         AND SECONDARY OPERATING MODES;     -   U.S. Provisional Patent Application No. 62/729,185, titled         POWERED STAPLING DEVICE THAT IS CAPABLE OF ADJUSTING FORCE,         ADVANCEMENT SPEED, AND OVERALL STROKE OF CUTTING MEMBER OF THE         DEVICE BASED ON SENSED PARAMETER OF FIRING OR CLAMPING;     -   U.S. Provisional Patent Application No. 62/729,184, titled         POWERED SURGICAL TOOL WITH A PREDEFINED ADJUSTABLE CONTROL         ALGORITHM FOR CONTROLLING AT LEAST ONE END EFFECTOR PARAMETER         AND A MEANS FOR LIMITING THE ADJUSTMENT;     -   U.S. Provisional Patent Application No. 62/729,182, titled         SENSING THE PATIENT POSITION AND CONTACT UTILIZING THE MONO         POLAR RETURN PAD ELECTRODE TO PROVIDE SITUATIONAL AWARENESS TO         THE HUB;     -   U.S. Provisional Patent Application No. 62/729,191, titled         SURGICAL NETWORK RECOMMENDATIONS FROM REAL TIME ANALYSIS OF         PROCEDURE VARIABLES AGAINST A BASELINE HIGHLIGHTING DIFFERENCES         FROM THE OPTIMAL SOLUTION;     -   U.S. Provisional Patent Application No. 62/729,195, titled         ULTRASONIC ENERGY DEVICE WHICH VARIES PRESSURE APPLIED BY CLAMP         ARM TO PROVIDE THRESHOLD CONTROL PRESSURE AT A CUT PROGRESSION         LOCATION; and     -   U.S. Provisional Patent Application No. 62/729,186, titled         WIRELESS PAIRING OF A SURGICAL DEVICE WITH ANOTHER DEVICE WITHIN         A STERILE SURGICAL FIELD BASED ON THE USAGE AND SITUATIONAL         AWARENESS OF DEVICES.

Applicant of the present application owns the following U.S. patent applications, filed on Aug. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. patent application Ser. No. 16/115,214, titled ESTIMATING         STATE OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR;     -   U.S. patent application Ser. No. 16/115,205, titled TEMPERATURE         CONTROL OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR;     -   U.S. patent application Ser. No. 16/115,233, titled RADIO         FREQUENCY ENERGY DEVICE FOR DELIVERING COMBINED ELECTRICAL         SIGNALS;     -   U.S. patent application Ser. No. 16/115,208, titled CONTROLLING         AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO TISSUE LOCATION;     -   U.S. patent application Ser. No. 16/115,220, titled CONTROLLING         ACTIVATION OF AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO THE         PRESENCE OF TISSUE;     -   U.S. patent application Ser. No. 16/115,232, titled DETERMINING         TISSUE COMPOSITION VIA AN ULTRASONIC SYSTEM;     -   U.S. patent application Ser. No. 16/115,239, titled DETERMINING         THE STATE OF AN ULTRASONIC ELECTROMECHANICAL SYSTEM ACCORDING TO         FREQUENCY SHIFT;     -   U.S. patent application Ser. No. 16/115,247, titled DETERMINING         THE STATE OF AN ULTRASONIC END EFFECTOR;     -   U.S. patent application Ser. No. 16/115,211, titled SITUATIONAL         AWARENESS OF ELECTROSURGICAL SYSTEMS;     -   U.S. patent application Ser. No. 16/115,226, titled MECHANISMS         FOR CONTROLLING DIFFERENT ELECTROMECHANICAL SYSTEMS OF AN         ELECTROSURGICAL INSTRUMENT;     -   U.S. patent application Ser. No. 16/115,240, titled DETECTION OF         END EFFECTOR IMMERSION IN LIQUID;     -   U.S. patent application Ser. No. 16/115,249, titled INTERRUPTION         OF ENERGY DUE TO INADVERTENT CAPACITIVE COUPLING;     -   U.S. patent application Ser. No. 16/115,256, titled INCREASING         RADIO FREQUENCY TO CREATE PAD-LESS MONOPOLAR LOOP;     -   U.S. patent application Ser. No. 16/115,223, titled BIPOLAR         COMBINATION DEVICE THAT AUTOMATICALLY ADJUSTS PRESSURE BASED ON         ENERGY MODALITY; and     -   U.S. patent application Ser. No. 16/115,238, titled ACTIVATION         OF ENERGY DEVICES.

Applicant of the present application owns the following U.S. patent applications, filed on Aug. 23, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application No. 62/721,995, titled         CONTROLLING AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO         TISSUE LOCATION;     -   U.S. Provisional Patent Application No. 62/721,998, titled         SITUATIONAL AWARENESS OF ELECTROSURGICAL SYSTEMS;     -   U.S. Provisional Patent Application No. 62/721,999, titled         INTERRUPTION OF ENERGY DUE TO INADVERTENT CAPACITIVE COUPLING;     -   U.S. Provisional Patent Application No. 62/721,994, titled         BIPOLAR COMBINATION DEVICE THAT AUTOMATICALLY ADJUSTS PRESSURE         BASED ON ENERGY MODALITY; and     -   U.S. Provisional Patent Application No. 62/721,996, titled RADIO         FREQUENCY ENERGY DEVICE FOR DELIVERING COMBINED ELECTRICAL         SIGNALS.

Applicant of the present application owns the following U.S. patent applications, filed on Jun. 30, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application No. 62/692,747, titled SMART         ACTIVATION OF AN ENERGY DEVICE BY ANOTHER DEVICE;     -   U.S. Provisional Patent Application No. 62/692,748, titled SMART         ENERGY ARCHITECTURE; and     -   U.S. Provisional Patent Application No. 62/692,768, titled SMART         ENERGY DEVICES.

Applicant of the present application owns the following U.S. patent applications, filed on Jun. 29, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. patent application Ser. No. 16/024,090, titled CAPACITIVE         COUPLED RETURN PATH PAD WITH SEPARABLE ARRAY ELEMENTS;     -   U.S. patent application Ser. No. 16/024,057, titled CONTROLLING         A SURGICAL INSTRUMENT ACCORDING TO SENSED CLOSURE PARAMETERS;     -   U.S. patent application Ser. No. 16/024,067, titled SYSTEMS FOR         ADJUSTING END EFFECTOR PARAMETERS BASED ON PERIOPERATIVE         INFORMATION;     -   U.S. patent application Ser. No. 16/024,075, titled SAFETY         SYSTEMS FOR SMART POWERED SURGICAL STAPLING;     -   U.S. patent application Ser. No. 16/024,083, titled SAFETY         SYSTEMS FOR SMART POWERED SURGICAL STAPLING;     -   U.S. patent application Ser. No. 16/024,094, titled SURGICAL         SYSTEMS FOR DETECTING END EFFECTOR TISSUE DISTRIBUTION         IRREGULARITIES;     -   U.S. patent application Ser. No. 16/024,138, titled SYSTEMS FOR         DETECTING PROXIMITY OF SURGICAL END EFFECTOR TO CANCEROUS         TISSUE;     -   U.S. patent application Ser. No. 16/024,150, titled SURGICAL         INSTRUMENT CARTRIDGE SENSOR ASSEMBLIES;     -   U.S. patent application Ser. No. 16/024,160, titled VARIABLE         OUTPUT CARTRIDGE SENSOR ASSEMBLY;     -   U.S. patent application Ser. No. 16/024,124, titled SURGICAL         INSTRUMENT HAVING A FLEXIBLE ELECTRODE;     -   U.S. patent application Ser. No. 16/024,132, titled SURGICAL         INSTRUMENT HAVING A FLEXIBLE CIRCUIT;     -   U.S. patent application Ser. No. 16/024,141, titled SURGICAL         INSTRUMENT WITH A TISSUE MARKING ASSEMBLY;     -   U.S. patent application Ser. No. 16/024,162, titled SURGICAL         SYSTEMS WITH PRIORITIZED DATA TRANSMISSION CAPABILITIES;     -   U.S. patent application Ser. No. 16/024,066, titled SURGICAL         EVACUATION SENSING AND MOTOR CONTROL;     -   U.S. patent application Ser. No. 16/024,096, titled SURGICAL         EVACUATION SENSOR ARRANGEMENTS;     -   U.S. patent application Ser. No. 16/024,116, titled SURGICAL         EVACUATION FLOW PATHS;     -   U.S. patent application Ser. No. 16/024,149, titled SURGICAL         EVACUATION SENSING AND GENERATOR CONTROL;     -   U.S. patent application Ser. No. 16/024,180, titled SURGICAL         EVACUATION SENSING AND DISPLAY;     -   U.S. patent application Ser. No. 16/024,245, titled         COMMUNICATION OF SMOKE EVACUATION SYSTEM PARAMETERS TO HUB OR         CLOUD IN SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL         PLATFORM;     -   U.S. patent application Ser. No. 16/024,258, titled SMOKE         EVACUATION SYSTEM INCLUDING A SEGMENTED CONTROL CIRCUIT FOR         INTERACTIVE SURGICAL PLATFORM;     -   U.S. patent application Ser. No. 16/024,265, titled SURGICAL         EVACUATION SYSTEM WITH A COMMUNICATION CIRCUIT FOR COMMUNICATION         BETWEEN A FILTER AND A SMOKE EVACUATION DEVICE; and     -   U.S. patent application Ser. No. 16/024,273, titled DUAL         IN-SERIES LARGE AND SMALL DROPLET FILTERS.

Applicant of the present application owns the following U.S. Provisional patent applications, filed on Jun. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. 62/691,228, titled         A METHOD OF USING REINFORCED FLEX CIRCUITS WITH MULTIPLE SENSORS         WITH ELECTROSURGICAL DEVICES;     -   U.S. Provisional Patent Application Ser. No. 62/691,227, titled         CONTROLLING A SURGICAL INSTRUMENT ACCORDING TO SENSED CLOSURE         PARAMETERS;     -   U.S. Provisional Patent Application Ser. No. 62/691,230, titled         SURGICAL INSTRUMENT HAVING A FLEXIBLE ELECTRODE;     -   U.S. Provisional Patent Application Ser. No. 62/691,219, titled         SURGICAL EVACUATION SENSING AND MOTOR CONTROL;     -   U.S. Provisional Patent Application Ser. No. 62/691,257, titled         COMMUNICATION OF SMOKE EVACUATION SYSTEM PARAMETERS TO HUB OR         CLOUD IN SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL         PLATFORM;     -   U.S. Provisional Patent Application Ser. No. 62/691,262, titled         SURGICAL EVACUATION SYSTEM WITH A COMMUNICATION CIRCUIT FOR         COMMUNICATION BETWEEN A FILTER AND A SMOKE EVACUATION DEVICE;         and     -   U.S. Provisional Patent Application Ser. No. 62/691,251, titled         DUAL IN-SERIES LARGE AND SMALL DROPLET FILTERS.

Applicant of the present application owns the following U.S. Provisional patent application, filed on Apr. 19, 2018, the disclosure of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. 62/659,900, titled         METHOD OF HUB COMMUNICATION.

Applicant of the present application owns the following U.S. Provisional patent applications, filed on Mar. 30, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application No. 62/650,898 filed on Mar.         30, 2018, titled CAPACITIVE COUPLED RETURN PATH PAD WITH         SEPARABLE ARRAY ELEMENTS;     -   U.S. Provisional Patent Application Ser. No. 62/650,887, titled         SURGICAL SYSTEMS WITH OPTIMIZED SENSING CAPABILITIES;     -   U.S. Provisional Patent Application Ser. No. 62/650,882, titled         SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL PLATFORM; and     -   U.S. Provisional Patent Application Ser. No. 62/650,877, titled         SURGICAL SMOKE EVACUATION SENSING AND CONTROLS.

Applicant of the present application owns the following U.S. patent applications, filed on Mar. 29, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. patent application Ser. No. 15/940,641, titled INTERACTIVE         SURGICAL SYSTEMS WITH ENCRYPTED COMMUNICATION CAPABILITIES;     -   U.S. patent application Ser. No. 15/940,648, titled INTERACTIVE         SURGICAL SYSTEMS WITH CONDITION HANDLING OF DEVICES AND DATA         CAPABILITIES;     -   U.S. patent application Ser. No. 15/940,656, titled SURGICAL HUB         COORDINATION OF CONTROL AND COMMUNICATION OF OPERATING ROOM         DEVICES;     -   U.S. patent application Ser. No. 15/940,666, titled SPATIAL         AWARENESS OF SURGICAL HUBS IN OPERATING ROOMS;     -   U.S. patent application Ser. No. 15/940,670, titled COOPERATIVE         UTILIZATION OF DATA DERIVED FROM SECONDARY SOURCES BY         INTELLIGENT SURGICAL HUBS;     -   U.S. patent application Ser. No. 15/940,677, titled SURGICAL HUB         CONTROL ARRANGEMENTS;     -   U.S. patent application Ser. No. 15/940,632, titled DATA         STRIPPING METHOD TO INTERROGATE PATIENT RECORDS AND CREATE         ANONYMIZED RECORD;     -   U.S. patent application Ser. No. 15/940,640, titled         COMMUNICATION HUB AND STORAGE DEVICE FOR STORING PARAMETERS AND         STATUS OF A SURGICAL DEVICE TO BE SHARED WITH CLOUD BASED         ANALYTICS SYSTEMS;     -   U.S. patent application Ser. No. 15/940,645, titled SELF         DESCRIBING DATA PACKETS GENERATED AT AN ISSUING INSTRUMENT;     -   U.S. patent application Ser. No. 15/940,649, titled DATA PAIRING         TO INTERCONNECT A DEVICE MEASURED PARAMETER WITH AN OUTCOME;     -   U.S. patent application Ser. No. 15/940,654, titled SURGICAL HUB         SITUATIONAL AWARENESS;     -   U.S. patent application Ser. No. 15/940,663, titled SURGICAL         SYSTEM DISTRIBUTED PROCESSING;     -   U.S. patent application Ser. No. 15/940,668, titled AGGREGATION         AND REPORTING OF SURGICAL HUB DATA;     -   U.S. patent application Ser. No. 15/940,671, titled SURGICAL HUB         SPATIAL AWARENESS TO DETERMINE DEVICES IN OPERATING THEATER;     -   U.S. patent application Ser. No. 15/940,686, titled DISPLAY OF         ALIGNMENT OF STAPLE CARTRIDGE TO PRIOR LINEAR STAPLE LINE;     -   U.S. patent application Ser. No. 15/940,700, titled STERILE         FIELD INTERACTIVE CONTROL DISPLAYS;     -   U.S. patent application Ser. No. 15/940,629, titled COMPUTER         IMPLEMENTED INTERACTIVE SURGICAL SYSTEMS;     -   U.S. patent application Ser. No. 15/940,704, titled USE OF LASER         LIGHT AND RED-GREEN-BLUE COLORATION TO DETERMINE PROPERTIES OF         BACK SCATTERED LIGHT;     -   U.S. patent application Ser. No. 15/940,722, titled         CHARACTERIZATION OF TISSUE IRREGULARITIES THROUGH THE USE OF         MONO-CHROMATIC LIGHT REFRACTIVITY;     -   U.S. patent application Ser. No. 15/940,742, titled DUAL CMOS         ARRAY IMAGING.     -   U.S. patent application Ser. No. 15/940,636, titled ADAPTIVE         CONTROL PROGRAM UPDATES FOR SURGICAL DEVICES;     -   U.S. patent application Ser. No. 15/940,653, titled ADAPTIVE         CONTROL PROGRAM UPDATES FOR SURGICAL HUBS;     -   U.S. patent application Ser. No. 15/940,660, titled CLOUD-BASED         MEDICAL ANALYTICS FOR CUSTOMIZATION AND RECOMMENDATIONS TO A         USER;     -   U.S. patent application Ser. No. 15/940,679, titled CLOUD-BASED         MEDICAL ANALYTICS FOR LINKING OF LOCAL USAGE TRENDS WITH THE         RESOURCE ACQUISITION BEHAVIORS OF LARGER DATA SET;     -   U.S. patent application Ser. No. 15/940,694, titled CLOUD-BASED         MEDICAL ANALYTICS FOR MEDICAL FACILITY SEGMENTED         INDIVIDUALIZATION OF INSTRUMENT FUNCTION;     -   U.S. patent application Ser. No. 15/940,634, titled CLOUD-BASED         MEDICAL ANALYTICS FOR SECURITY AND AUTHENTICATION TRENDS AND         REACTIVE MEASURES;     -   U.S. patent application Ser. No. 15/940,706, titled DATA         HANDLING AND PRIORITIZATION IN A CLOUD ANALYTICS NETWORK;     -   U.S. patent application Ser. No. 15/940,675, titled CLOUD         INTERFACE FOR COUPLED SURGICAL DEVICES;     -   U.S. patent application Ser. No. 15/940,627, titled DRIVE         ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,637, titled         COMMUNICATION ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL         PLATFORMS;     -   U.S. patent application Ser. No. 15/940,642, titled CONTROLS FOR         ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,676, titled AUTOMATIC         TOOL ADJUSTMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,680, titled CONTROLLERS         FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,683, titled COOPERATIVE         SURGICAL ACTIONS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,690, titled DISPLAY         ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS; and     -   U.S. patent application Ser. No. 15/940,711, titled SENSING         ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS.

Applicant of the present application owns the following U.S. Provisional patent applications, filed on Mar. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. 62/649,302, titled         INTERACTIVE SURGICAL SYSTEMS WITH ENCRYPTED COMMUNICATION         CAPABILITIES;     -   U.S. Provisional Patent Application Ser. No. 62/649,294, titled         DATA STRIPPING METHOD TO INTERROGATE PATIENT RECORDS AND CREATE         ANONYMIZED RECORD;     -   U.S. Provisional Patent Application Ser. No. 62/649,300, titled         SURGICAL HUB SITUATIONAL AWARENESS;     -   U.S. Provisional Patent Application Ser. No. 62/649,309, titled         SURGICAL HUB SPATIAL AWARENESS TO DETERMINE DEVICES IN OPERATING         THEATER;     -   U.S. Provisional Patent Application Ser. No. 62/649,310, titled         COMPUTER IMPLEMENTED INTERACTIVE SURGICAL SYSTEMS;     -   U.S. Provisional Patent Application Ser. No. 62/649,291, titled         USE OF LASER LIGHT AND RED-GREEN-BLUE COLORATION TO DETERMINE         PROPERTIES OF BACK SCATTERED LIGHT;     -   U.S. Provisional Patent Application Ser. No. 62/649,296, titled         ADAPTIVE CONTROL PROGRAM UPDATES FOR SURGICAL DEVICES;     -   U.S. Provisional Patent Application Ser. No. 62/649,333, titled         CLOUD-BASED MEDICAL ANALYTICS FOR CUSTOMIZATION AND         RECOMMENDATIONS TO A USER;     -   U.S. Provisional Patent Application Ser. No. 62/649,327, titled         CLOUD-BASED MEDICAL ANALYTICS FOR SECURITY AND AUTHENTICATION         TRENDS AND REACTIVE MEASURES;     -   U.S. Provisional Patent Application Ser. No. 62/649,315, titled         DATA HANDLING AND PRIORITIZATION IN A CLOUD ANALYTICS NETWORK;     -   U.S. Provisional Patent Application Ser. No. 62/649,313, titled         CLOUD INTERFACE FOR COUPLED SURGICAL DEVICES;     -   U.S. Provisional Patent Application Ser. No. 62/649,320, titled         DRIVE ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. Provisional Patent Application Ser. No. 62/649,307, titled         AUTOMATIC TOOL ADJUSTMENTS FOR ROBOT-ASSISTED SURGICAL         PLATFORMS; and     -   U.S. Provisional Patent Application Ser. No. 62/649,323, titled         SENSING ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS.

Applicant of the present application owns the following U.S. Provisional patent applications, filed on Mar. 8, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. 62/640,417, titled         TEMPERATURE CONTROL IN ULTRASONIC DEVICE AND CONTROL SYSTEM         THEREFOR; and     -   U.S. Provisional Patent Application Ser. No. 62/640,415, titled         ESTIMATING STATE OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM         THEREFOR.

Applicant of the present application owns the following U.S. Provisional patent applications, filed on Dec. 28, 2017, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional patent application Serial No. U.S. Provisional         Patent Application Ser. No. 62/611,341, titled INTERACTIVE         SURGICAL PLATFORM;     -   U.S. Provisional Patent Application Ser. No. 62/611,340, titled         CLOUD-BASED MEDICAL ANALYTICS; and     -   U.S. Provisional Patent Application Ser. No. 62/611,339, titled         ROBOT ASSISTED SURGICAL PLATFORM.

Before explaining various aspects of surgical devices and generators in detail, it should be noted that the illustrative examples are not limited in application or use to the details of construction and arrangement of parts illustrated in the accompanying drawings and description. The illustrative examples may be implemented or incorporated in other aspects, variations and modifications, and may be practiced or carried out in various ways. Further, unless otherwise indicated, the terms and expressions employed herein have been chosen for the purpose of describing the illustrative examples for the convenience of the reader and are not for the purpose of limitation thereof. Also, it will be appreciated that one or more of the following-described aspects, expressions of aspects, and/or examples, can be combined with any one or more of the other following-described aspects, expressions of aspects and/or examples.

Surgical Hubs

Referring to FIG. 1, a computer-implemented interactive surgical system 100 includes one or more surgical systems 102 and a cloud-based system (e.g., the cloud 104 that may include a remote server 113 coupled to a storage device 105). Each surgical system 102 includes at least one surgical hub 106 in communication with the cloud 104 that may include a remote server 113. In one example, as illustrated in FIG. 1, the surgical system 102 includes a visualization system 108, a robotic system 110, and a handheld intelligent surgical instrument 112, which are configured to communicate with one another and/or the hub 106. In some aspects, a surgical system 102 may include an M number of hubs 106, an N number of visualization systems 108, an O number of robotic systems 110, and a P number of handheld intelligent surgical instruments 112, where M, N, O, and P are integers greater than or equal to one.

FIG. 2 depicts an example of a surgical system 102 being used to perform a surgical procedure on a patient who is lying down on an operating table 114 in a surgical operating room 116. A robotic system 110 is used in the surgical procedure as a part of the surgical system 102. The robotic system 110 includes a surgeon's console 118, a patient side cart 120 (surgical robot), and a surgical robotic hub 122. The patient side cart 120 can manipulate at least one removably coupled surgical tool 117 through a minimally invasive incision in the body of the patient while the surgeon views the surgical site through the surgeon's console 118. An image of the surgical site can be obtained by a medical imaging device 124, which can be manipulated by the patient side cart 120 to orient the imaging device 124. The robotic hub 122 can be used to process the images of the surgical site for subsequent display to the surgeon through the surgeon's console 118.

Other types of robotic systems can be readily adapted for use with the surgical system 102. Various examples of robotic systems and surgical tools that are suitable for use with the present disclosure are described in U.S. Provisional Patent Application Ser. No. 62/611,339, titled ROBOT ASSISTED SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety.

Various examples of cloud-based analytics that are performed by the cloud 104, and are suitable for use with the present disclosure, are described in U.S. Provisional Patent Application Ser. No. 62/611,340, titled CLOUD-BASED MEDICAL ANALYTICS, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety.

In various aspects, the imaging device 124 includes at least one image sensor and one or more optical components. Suitable image sensors include, but are not limited to, Charge-Coupled Device (CCD) sensors and Complementary Metal-Oxide Semiconductor (CMOS) sensors.

The optical components of the imaging device 124 may include one or more illumination sources and/or one or more lenses. The one or more illumination sources may be directed to illuminate portions of the surgical field. The one or more image sensors may receive light reflected or refracted from the surgical field, including light reflected or refracted from tissue and/or surgical instruments.

The one or more illumination sources may be configured to radiate electromagnetic energy in the visible spectrum as well as the invisible spectrum. The visible spectrum, sometimes referred to as the optical spectrum or luminous spectrum, is that portion of the electromagnetic spectrum that is visible to (i.e., can be detected by) the human eye and may be referred to as visible light or simply light. A typical human eye will respond to wavelengths in air that are from about 380 nm to about 750 nm.

The invisible spectrum (i.e., the non-luminous spectrum) is that portion of the electromagnetic spectrum that lies below and above the visible spectrum (i.e., wavelengths below about 380 nm and above about 750 nm). The invisible spectrum is not detectable by the human eye. Wavelengths greater than about 750 nm are longer than the red visible spectrum, and they become invisible infrared (IR), microwave, and radio electromagnetic radiation. Wavelengths less than about 380 nm are shorter than the violet spectrum, and they become invisible ultraviolet, x-ray, and gamma ray electromagnetic radiation.

In various aspects, the imaging device 124 is configured for use in a minimally invasive procedure. Examples of imaging devices suitable for use with the present disclosure include, but not limited to, an arthroscope, angioscope, bronchoscope, choledochoscope, colonoscope, cytoscope, duodenoscope, enteroscope, esophagogastro-duodenoscope (gastroscope), endoscope, laryngoscope, nasopharyngo-neproscope, sigmoidoscope, thoracoscope, and ureteroscope.

In one aspect, the imaging device employs multi-spectrum monitoring to discriminate topography and underlying structures. A multi-spectral image is one that captures image data within specific wavelength ranges across the electromagnetic spectrum. The wavelengths may be separated by filters or by the use of instruments that are sensitive to particular wavelengths, including light from frequencies beyond the visible light range, e.g., IR and ultraviolet. Spectral imaging can allow extraction of additional information the human eye fails to capture with its receptors for red, green, and blue. The use of multi-spectral imaging is described in greater detail under the heading “Advanced Imaging Acquisition Module” in U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety. Multi-spectrum monitoring can be a useful tool in relocating a surgical field after a surgical task is completed to perform one or more of the previously described tests on the treated tissue.

It is axiomatic that strict sterilization of the operating room and surgical equipment is required during any surgery. The strict hygiene and sterilization conditions required in a “surgical theater,” i.e., an operating or treatment room, necessitate the highest possible sterility of all medical devices and equipment. Part of that sterilization process is the need to sterilize anything that comes in contact with the patient or penetrates the sterile field, including the imaging device 124 and its attachments and components. It will be appreciated that the sterile field may be considered a specified area, such as within a tray or on a sterile towel, that is considered free of microorganisms, or the sterile field may be considered an area, immediately around a patient, who has been prepared for a surgical procedure. The sterile field may include the scrubbed team members, who are properly attired, and all furniture and fixtures in the area.

In various aspects, the visualization system 108 includes one or more imaging sensors, one or more image-processing units, one or more storage arrays, and one or more displays that are strategically arranged with respect to the sterile field, as illustrated in FIG. 2. In one aspect, the visualization system 108 includes an interface for HL7, PACS, and EMR. Various components of the visualization system 108 are described under the heading “Advanced Imaging Acquisition Module” in U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety.

As illustrated in FIG. 2, a primary display 119 is positioned in the sterile field to be visible to an operator at the operating table 114. In addition, a visualization tower 111 is positioned outside the sterile field. The visualization tower 111 includes a first non-sterile display 107 and a second non-sterile display 109, which face away from each other. The visualization system 108, guided by the hub 106, is configured to utilize the displays 107, 109, and 119 to coordinate information flow to operators inside and outside the sterile field. For example, the hub 106 may cause the visualization system 108 to display a snapshot of a surgical site, as recorded by an imaging device 124, on a non-sterile display 107 or 109, while maintaining a live feed of the surgical site on the primary display 119. The snapshot on the non-sterile display 107 or 109 can permit a non-sterile operator to perform a diagnostic step relevant to the surgical procedure, for example.

In one aspect, the hub 106 is also configured to route a diagnostic input or feedback entered by a non-sterile operator at the visualization tower 111 to the primary display 119 within the sterile field, where it can be viewed by a sterile operator at the operating table. In one example, the input can be in the form of a modification to the snapshot displayed on the non-sterile display 107 or 109, which can be routed to the primary display 119 by the hub 106.

Referring to FIG. 2, a surgical instrument 112 is being used in the surgical procedure as part of the surgical system 102. The hub 106 is also configured to coordinate information flow to a display of the surgical instrument 112. For example, coordinate information flow is further described in U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety. A diagnostic input or feedback entered by a non-sterile operator at the visualization tower 111 can be routed by the hub 106 to the surgical instrument display 115 within the sterile field, where it can be viewed by the operator of the surgical instrument 112. Example surgical instruments that are suitable for use with the surgical system 102 are described under the heading “Surgical Instrument Hardware” in U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, the disclosure of which is herein incorporated by reference in its entirety, for example.

Referring now to FIG. 3, a hub 106 is depicted in communication with a visualization system 108, a robotic system 110, and a handheld intelligent surgical instrument 112. The hub 106 includes a hub display 135, an imaging module 138, a generator module 140 (which can include a monopolar generator 142, a bipolar generator 144, and/or an ultrasonic generator 143), a communication module 130, a processor module 132, and a storage array 134. In certain aspects, as illustrated in FIG. 3, the hub 106 further includes a smoke evacuation module 126, a suction/irrigation module 128, and/or an OR mapping module 133.

During a surgical procedure, energy application to tissue, for sealing and/or cutting, is generally associated with smoke evacuation, suction of excess fluid, and/or irrigation of the tissue. Fluid, power, and/or data lines from different sources are often entangled during the surgical procedure. Valuable time can be lost addressing this issue during a surgical procedure. Detangling the lines may necessitate disconnecting the lines from their respective modules, which may require resetting the modules. The hub modular enclosure 136 offers a unified environment for managing the power, data, and fluid lines, which reduces the frequency of entanglement between such lines.

Aspects of the present disclosure present a surgical hub for use in a surgical procedure that involves energy application to tissue at a surgical site. The surgical hub includes a hub enclosure and a combo generator module slidably receivable in a docking station of the hub enclosure. The docking station includes data and power contacts. The combo generator module includes two or more of an ultrasonic energy generator component, a bipolar RF energy generator component, and a monopolar RF energy generator component that are housed in a single unit. In one aspect, the combo generator module also includes a smoke evacuation component, at least one energy delivery cable for connecting the combo generator module to a surgical instrument, at least one smoke evacuation component configured to evacuate smoke, fluid, and/or particulates generated by the application of therapeutic energy to the tissue, and a fluid line extending from the remote surgical site to the smoke evacuation component.

In one aspect, the fluid line is a first fluid line and a second fluid line extends from the remote surgical site to a suction and irrigation module slidably received in the hub enclosure. In one aspect, the hub enclosure comprises a fluid interface.

Certain surgical procedures may require the application of more than one energy type to the tissue. One energy type may be more beneficial for cutting the tissue, while another different energy type may be more beneficial for sealing the tissue. For example, a bipolar generator can be used to seal the tissue while an ultrasonic generator can be used to cut the sealed tissue. Aspects of the present disclosure present a solution where a hub modular enclosure 136 is configured to accommodate different generators, and facilitate an interactive communication therebetween. One of the advantages of the hub modular enclosure 136 is enabling the quick removal and/or replacement of various modules.

Aspects of the present disclosure present a modular surgical enclosure for use in a surgical procedure that involves energy application to tissue. The modular surgical enclosure includes a first energy-generator module, configured to generate a first energy for application to the tissue, and a first docking station comprising a first docking port that includes first data and power contacts, wherein the first energy-generator module is slidably movable into an electrical engagement with the power and data contacts and wherein the first energy-generator module is slidably movable out of the electrical engagement with the first power and data contacts,

Further to the above, the modular surgical enclosure also includes a second energy-generator module configured to generate a second energy, different than the first energy, for application to the tissue, and a second docking station comprising a second docking port that includes second data and power contacts, wherein the second energy-generator module is slidably movable into an electrical engagement with the power and data contacts, and wherein the second energy-generator module is slidably movable out of the electrical engagement with the second power and data contacts.

In addition, the modular surgical enclosure also includes a communication bus between the first docking port and the second docking port, configured to facilitate communication between the first energy-generator module and the second energy-generator module.

Referring to FIGS. 3-7, aspects of the present disclosure are presented for a hub modular enclosure 136 that allows the modular integration of a generator module 140, a smoke evacuation module 126, and a suction/irrigation module 128. The hub modular enclosure 136 further facilitates interactive communication between the modules 140, 126, 128. As illustrated in FIG. 5, the generator module 140 can be a generator module with integrated monopolar, bipolar, and ultrasonic components supported in a single housing unit 139 slidably insertable into the hub modular enclosure 136. As illustrated in FIG. 5, the generator module 140 can be configured to connect to a monopolar device 146, a bipolar device 147, and an ultrasonic device 148. Alternatively, the generator module 140 may comprise a series of monopolar, bipolar, and/or ultrasonic generator modules that interact through the hub modular enclosure 136. The hub modular enclosure 136 can be configured to facilitate the insertion of multiple generators and interactive communication between the generators docked into the hub modular enclosure 136 so that the generators would act as a single generator.

In one aspect, the hub modular enclosure 136 comprises a modular power and communication backplane 149 with external and wireless communication headers to enable the removable attachment of the modules 140, 126, 128 and interactive communication therebetween.

In one aspect, the hub modular enclosure 136 includes docking stations, or drawers, 151, herein also referred to as drawers, which are configured to slidably receive the modules 140, 126, 128. FIG. 4 illustrates a partial perspective view of a surgical hub enclosure 136, and a combo generator module 145 slidably receivable in a docking station 151 of the surgical hub enclosure 136. A docking port 152 with power and data contacts on a rear side of the combo generator module 145 is configured to engage a corresponding docking port 150 with power and data contacts of a corresponding docking station 151 of the hub modular enclosure 136 as the combo generator module 145 is slid into position within the corresponding docking station 151 of the hub module enclosure 136. In one aspect, the combo generator module 145 includes a bipolar, ultrasonic, and monopolar module and a smoke evacuation module integrated together into a single housing unit 139, as illustrated in FIG. 5.

In various aspects, the smoke evacuation module 126 includes a fluid line 154 that conveys captured/collected smoke and/or fluid away from a surgical site and to, for example, the smoke evacuation module 126. Vacuum suction originating from the smoke evacuation module 126 can draw the smoke into an opening of a utility conduit at the surgical site. The utility conduit, coupled to the fluid line, can be in the form of a flexible tube terminating at the smoke evacuation module 126. The utility conduit and the fluid line define a fluid path extending toward the smoke evacuation module 126 that is received in the hub enclosure 136.

In various aspects, the suction/irrigation module 128 is coupled to a surgical tool comprising an aspiration fluid line and a suction fluid line. In one example, the aspiration and suction fluid lines are in the form of flexible tubes extending from the surgical site toward the suction/irrigation module 128. One or more drive systems can be configured to cause irrigation and aspiration of fluids to and from the surgical site.

In one aspect, the surgical tool includes a shaft having an end effector at a distal end thereof and at least one energy treatment associated with the end effector, an aspiration tube, and an irrigation tube. The aspiration tube can have an inlet port at a distal end thereof and the aspiration tube extends through the shaft. Similarly, an irrigation tube can extend through the shaft and can have an inlet port in proximity to the energy deliver implement. The energy deliver implement is configured to deliver ultrasonic and/or RF energy to the surgical site and is coupled to the generator module 140 by a cable extending initially through the shaft.

The irrigation tube can be in fluid communication with a fluid source, and the aspiration tube can be in fluid communication with a vacuum source. The fluid source and/or the vacuum source can be housed in the suction/irrigation module 128. In one example, the fluid source and/or the vacuum source can be housed in the hub enclosure 136 separately from the suction/irrigation module 128. In such example, a fluid interface can be configured to connect the suction/irrigation module 128 to the fluid source and/or the vacuum source.

In one aspect, the modules 140, 126, 128 and/or their corresponding docking stations on the hub modular enclosure 136 may include alignment features that are configured to align the docking ports of the modules into engagement with their counterparts in the docking stations of the hub modular enclosure 136. For example, as illustrated in FIG. 4, the combo generator module 145 includes side brackets 155 that are configured to slidably engage with corresponding brackets 156 of the corresponding docking station 151 of the hub modular enclosure 136. The brackets cooperate to guide the docking port contacts of the combo generator module 145 into an electrical engagement with the docking port contacts of the hub modular enclosure 136.

In some aspects, the drawers 151 of the hub modular enclosure 136 are the same, or substantially the same size, and the modules are adjusted in size to be received in the drawers 151. For example, the side brackets 155 and/or 156 can be larger or smaller depending on the size of the module. In other aspects, the drawers 151 are different in size and are each designed to accommodate a particular module.

Furthermore, the contacts of a particular module can be keyed for engagement with the contacts of a particular drawer to avoid inserting a module into a drawer with mismatching contacts.

As illustrated in FIG. 4, the docking port 150 of one drawer 151 can be coupled to the docking port 150 of another drawer 151 through a communications link 157 to facilitate an interactive communication between the modules housed in the hub modular enclosure 136. The docking ports 150 of the hub modular enclosure 136 may alternatively, or additionally, facilitate a wireless interactive communication between the modules housed in the hub modular enclosure 136. Any suitable wireless communication can be employed, such as for example Air Titan-Bluetooth.

FIG. 6 illustrates individual power bus attachments for a plurality of lateral docking ports of a lateral modular housing 160 configured to receive a plurality of modules of a surgical hub 206. The lateral modular housing 160 is configured to laterally receive and interconnect the modules 161. The modules 161 are slidably inserted into docking stations 162 of lateral modular housing 160, which includes a backplane for interconnecting the modules 161. As illustrated in FIG. 6, the modules 161 are arranged laterally in the lateral modular housing 160. Alternatively, the modules 161 may be arranged vertically in a lateral modular housing.

FIG. 7 illustrates a vertical modular housing 164 configured to receive a plurality of modules 165 of the surgical hub 106. The modules 165 are slidably inserted into docking stations, or drawers, 167 of vertical modular housing 164, which includes a backplane for interconnecting the modules 165. Although the drawers 167 of the vertical modular housing 164 are arranged vertically, in certain instances, a vertical modular housing 164 may include drawers that are arranged laterally. Furthermore, the modules 165 may interact with one another through the docking ports of the vertical modular housing 164. In the example of FIG. 7, a display 177 is provided for displaying data relevant to the operation of the modules 165. In addition, the vertical modular housing 164 includes a master module 178 housing a plurality of sub-modules that are slidably received in the master module 178.

In various aspects, the imaging module 138 comprises an integrated video processor and a modular light source and is adapted for use with various imaging devices. In one aspect, the imaging device is comprised of a modular housing that can be assembled with a light source module and a camera module. The housing can be a disposable housing. In at least one example, the disposable housing is removably coupled to a reusable controller, a light source module, and a camera module. The light source module and/or the camera module can be selectively chosen depending on the type of surgical procedure. In one aspect, the camera module comprises a CCD sensor. In another aspect, the camera module comprises a CMOS sensor. In another aspect, the camera module is configured for scanned beam imaging. Likewise, the light source module can be configured to deliver a white light or a different light, depending on the surgical procedure.

During a surgical procedure, removing a surgical device from the surgical field and replacing it with another surgical device that includes a different camera or a different light source can be inefficient. Temporarily losing sight of the surgical field may lead to undesirable consequences. The module imaging device of the present disclosure is configured to permit the replacement of a light source module or a camera module midstream during a surgical procedure, without having to remove the imaging device from the surgical field.

In one aspect, the imaging device comprises a tubular housing that includes a plurality of channels. A first channel is configured to slidably receive the camera module, which can be configured for a snap-fit engagement with the first channel. A second channel is configured to slidably receive the light source module, which can be configured for a snap-fit engagement with the second channel. In another example, the camera module and/or the light source module can be rotated into a final position within their respective channels. A threaded engagement can be employed in lieu of the snap-fit engagement.

In various examples, multiple imaging devices are placed at different positions in the surgical field to provide multiple views. The imaging module 138 can be configured to switch between the imaging devices to provide an optimal view. In various aspects, the imaging module 138 can be configured to integrate the images from the different imaging device.

Various image processors and imaging devices suitable for use with the present disclosure are described in U.S. Pat. No. 7,995,045, titled COMBINED SBI AND CONVENTIONAL IMAGE PROCESSOR, which issued on Aug. 9, 2011, which is herein incorporated by reference in its entirety. In addition, U.S. Pat. No. 7,982,776, titled SBI MOTION ARTIFACT REMOVAL APPARATUS AND METHOD, which issued on Jul. 19, 2011, which is herein incorporated by reference in its entirety, describes various systems for removing motion artifacts from image data. Such systems can be integrated with the imaging module 138. Furthermore, U.S. Patent Application Publication No. 2011/0306840, titled CONTROLLABLE MAGNETIC SOURCE TO FIXTURE INTRACORPOREAL APPARATUS, which published on Dec. 15, 2011, and U.S. Patent Application Publication No. 2014/0243597, titled SYSTEM FOR PERFORMING A MINIMALLY INVASIVE SURGICAL PROCEDURE, which published on Aug. 28, 2014, each of which is herein incorporated by reference in its entirety.

FIG. 8 illustrates a surgical data network 201 comprising a modular communication hub 203 configured to connect modular devices located in one or more operating theaters of a healthcare facility, or any room in a healthcare facility specially equipped for surgical operations, to a cloud-based system (e.g., the cloud 204 that may include a remote server 213 coupled to a storage device 205). In one aspect, the modular communication hub 203 comprises a network hub 207 and/or a network switch 209 in communication with a network router. The modular communication hub 203 also can be coupled to a local computer system 210 to provide local computer processing and data manipulation. The surgical data network 201 may be configured as passive, intelligent, or switching. A passive surgical data network serves as a conduit for the data, enabling it to go from one device (or segment) to another and to the cloud computing resources. An intelligent surgical data network includes additional features to enable the traffic passing through the surgical data network to be monitored and to configure each port in the network hub 207 or network switch 209. An intelligent surgical data network may be referred to as a manageable hub or switch. A switching hub reads the destination address of each packet and then forwards the packet to the correct port.

Modular devices 1 a-1 n located in the operating theater may be coupled to the modular communication hub 203. The network hub 207 and/or the network switch 209 may be coupled to a network router 211 to connect the devices 1 a-1 n to the cloud 204 or the local computer system 210. Data associated with the devices 1 a-1 n may be transferred to cloud-based computers via the router for remote data processing and manipulation. Data associated with the devices 1 a-1 n may also be transferred to the local computer system 210 for local data processing and manipulation. Modular devices 2 a-2 m located in the same operating theater also may be coupled to a network switch 209. The network switch 209 may be coupled to the network hub 207 and/or the network router 211 to connect to the devices 2 a-2 m to the cloud 204. Data associated with the devices 2 a-2 n may be transferred to the cloud 204 via the network router 211 for data processing and manipulation. Data associated with the devices 2 a-2 m may also be transferred to the local computer system 210 for local data processing and manipulation.

It will be appreciated that the surgical data network 201 may be expanded by interconnecting multiple network hubs 207 and/or multiple network switches 209 with multiple network routers 211. The modular communication hub 203 may be contained in a modular control tower configured to receive multiple devices 1 a-1 n/2 a-2 m. The local computer system 210 also may be contained in a modular control tower. The modular communication hub 203 is connected to a display 212 to display images obtained by some of the devices 1 a-1 n/2 a-2 m, for example during surgical procedures. In various aspects, the devices 1 a-1 n/2 a-2 m may include, for example, various modules such as an imaging module 138 coupled to an endoscope, a generator module 140 coupled to an energy-based surgical device, a smoke evacuation module 126, a suction/irrigation module 128, a communication module 130, a processor module 132, a storage array 134, a surgical device coupled to a display, and/or a non-contact sensor module, among other modular devices that may be connected to the modular communication hub 203 of the surgical data network 201.

In one aspect, the surgical data network 201 may comprise a combination of network hub(s), network switch(es), and network router(s) connecting the devices 1 a-1 n/2 a-2 m to the cloud. Any one of or all of the devices 1 a-1 n/2 a-2 m coupled to the network hub or network switch may collect data in real time and transfer the data to cloud computers for data processing and manipulation. It will be appreciated that cloud computing relies on sharing computing resources rather than having local servers or personal devices to handle software applications. The word “cloud” may be used as a metaphor for “the Internet,” although the term is not limited as such. Accordingly, the term “cloud computing” may be used herein to refer to “a type of Internet-based computing,” where different services—such as servers, storage, and applications—are delivered to the modular communication hub 203 and/or computer system 210 located in the surgical theater (e.g., a fixed, mobile, temporary, or field operating room or space) and to devices connected to the modular communication hub 203 and/or computer system 210 through the Internet. The cloud infrastructure may be maintained by a cloud service provider. In this context, the cloud service provider may be the entity that coordinates the usage and control of the devices 1 a-1 n/2 a-2 m located in one or more operating theaters. The cloud computing services can perform a large number of calculations based on the data gathered by smart surgical instruments, robots, and other computerized devices located in the operating theater. The hub hardware enables multiple devices or connections to be connected to a computer that communicates with the cloud computing resources and storage.

Applying cloud computer data processing techniques on the data collected by the devices 1 a-1 n/2 a-2 m, the surgical data network provides improved surgical outcomes, reduced costs, and improved patient satisfaction. At least some of the devices 1 a-1 n/2 a-2 m may be employed to view tissue states to assess leaks or perfusion of sealed tissue after a tissue sealing and cutting procedure. At least some of the devices 1 a-1 n/2 a-2 m may be employed to identify pathology, such as the effects of diseases, using the cloud-based computing to examine data including images of samples of body tissue for diagnostic purposes. This includes localization and margin confirmation of tissue and phenotypes. At least some of the devices 1 a-1 n/2 a-2 m may be employed to identify anatomical structures of the body using a variety of sensors integrated with imaging devices and techniques such as overlaying images captured by multiple imaging devices. The data gathered by the devices 1 a-1 n/2 a-2 m, including image data, may be transferred to the cloud 204 or the local computer system 210 or both for data processing and manipulation including image processing and manipulation. The data may be analyzed to improve surgical procedure outcomes by determining if further treatment, such as the application of endoscopic intervention, emerging technologies, a targeted radiation, targeted intervention, and precise robotics to tissue-specific sites and conditions, may be pursued. Such data analysis may further employ outcome analytics processing, and using standardized approaches may provide beneficial feedback to either confirm surgical treatments and the behavior of the surgeon or suggest modifications to surgical treatments and the behavior of the surgeon.

In one implementation, the operating theater devices 1 a-1 n may be connected to the modular communication hub 203 over a wired channel or a wireless channel depending on the configuration of the devices 1 a-1 n to a network hub. The network hub 207 may be implemented, in one aspect, as a local network broadcast device that works on the physical layer of the Open System Interconnection (OSI) model. The network hub provides connectivity to the devices 1 a-1 n located in the same operating theater network. The network hub 207 collects data in the form of packets and sends them to the router in half duplex mode. The network hub 207 does not store any media access control/Internet Protocol (MAC/IP) to transfer the device data. Only one of the devices 1 a-1 n can send data at a time through the network hub 207. The network hub 207 has no routing tables or intelligence regarding where to send information and broadcasts all network data across each connection and to a remote server 213 (FIG. 9) over the cloud 204. The network hub 207 can detect basic network errors such as collisions, but having all information broadcast to multiple ports can be a security risk and cause bottlenecks.

In another implementation, the operating theater devices 2 a-2 m may be connected to a network switch 209 over a wired channel or a wireless channel. The network switch 209 works in the data link layer of the OSI model. The network switch 209 is a multicast device for connecting the devices 2 a-2 m located in the same operating theater to the network. The network switch 209 sends data in the form of frames to the network router 211 and works in full duplex mode. Multiple devices 2 a-2 m can send data at the same time through the network switch 209. The network switch 209 stores and uses MAC addresses of the devices 2 a-2 m to transfer data.

The network hub 207 and/or the network switch 209 are coupled to the network router 211 for connection to the cloud 204. The network router 211 works in the network layer of the OSI model. The network router 211 creates a route for transmitting data packets received from the network hub 207 and/or network switch 211 to cloud-based computer resources for further processing and manipulation of the data collected by any one of or all the devices 1 a-1 n/2 a-2 m. The network router 211 may be employed to connect two or more different networks located in different locations, such as, for example, different operating theaters of the same healthcare facility or different networks located in different operating theaters of different healthcare facilities. The network router 211 sends data in the form of packets to the cloud 204 and works in full duplex mode. Multiple devices can send data at the same time. The network router 211 uses IP addresses to transfer data.

In one example, the network hub 207 may be implemented as a USB hub, which allows multiple USB devices to be connected to a host computer. The USB hub may expand a single USB port into several tiers so that there are more ports available to connect devices to the host system computer. The network hub 207 may include wired or wireless capabilities to receive information over a wired channel or a wireless channel. In one aspect, a wireless USB short-range, high-bandwidth wireless radio communication protocol may be employed for communication between the devices 1 a-1 n and devices 2 a-2 m located in the operating theater.

In other examples, the operating theater devices 1 a-1 n/2 a-2 m may communicate to the modular communication hub 203 via Bluetooth wireless technology standard for exchanging data over short distances (using short-wavelength UHF radio waves in the ISM band from 2.4 to 2.485 GHz) from fixed and mobile devices and building personal area networks (PANs). In other aspects, the operating theater devices 1 a-1 n/2 a-2 m may communicate to the modular communication hub 203 via a number of wireless or wired communication standards or protocols, including but not limited to W-Fi (IEEE 802.11 family), WiMAX (IEEE 802.16 family), IEEE 802.20, long-term evolution (LTE), and Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT, and Ethernet derivatives thereof, as well as any other wireless and wired protocols that are designated as 3G, 4G, 5G, and beyond. The computing module may include a plurality of communication modules. For instance, a first communication module may be dedicated to shorter-range wireless communications such as Wi-Fi and Bluetooth, and a second communication module may be dedicated to longer-range wireless communications such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, and others.

The modular communication hub 203 may serve as a central connection for one or all of the operating theater devices 1 a-1 n/2 a-2 m and handles a data type known as frames. Frames carry the data generated by the devices 1 a-1 n/2 a-2 m. When a frame is received by the modular communication hub 203, it is amplified and transmitted to the network router 211, which transfers the data to the cloud computing resources by using a number of wireless or wired communication standards or protocols, as described herein.

The modular communication hub 203 can be used as a standalone device or be connected to compatible network hubs and network switches to form a larger network. The modular communication hub 203 is generally easy to install, configure, and maintain, making it a good option for networking the operating theater devices 1 a-1 n/2 a-2 m.

FIG. 9 illustrates a computer-implemented interactive surgical system 200. The computer-implemented interactive surgical system 200 is similar in many respects to the computer-implemented interactive surgical system 100. For example, the computer-implemented interactive surgical system 200 includes one or more surgical systems 202, which are similar in many respects to the surgical systems 102. Each surgical system 202 includes at least one surgical hub 206 in communication with a cloud 204 that may include a remote server 213. In one aspect, the computer-implemented interactive surgical system 200 comprises a modular control tower 236 connected to multiple operating theater devices such as, for example, intelligent surgical instruments, robots, and other computerized devices located in the operating theater. As shown in FIG. 10, the modular control tower 236 comprises a modular communication hub 203 coupled to a computer system 210. As illustrated in the example of FIG. 9, the modular control tower 236 is coupled to an imaging module 238 that is coupled to an endoscope 239, a generator module 240 that is coupled to an energy device 241, a smoke evacuator module 226, a suction/irrigation module 228, a communication module 230, a processor module 232, a storage array 234, a smart device/instrument 235 optionally coupled to a display 237, and a non-contact sensor module 242. The operating theater devices are coupled to cloud computing resources and data storage via the modular control tower 236. A robot hub 222 also may be connected to the modular control tower 236 and to the cloud computing resources. The devices/instruments 235, visualization systems 208, among others, may be coupled to the modular control tower 236 via wired or wireless communication standards or protocols, as described herein. The modular control tower 236 may be coupled to a hub display 215 (e.g., monitor, screen) to display and overlay images received from the imaging module, device/instrument display, and/or other visualization systems 208. The hub display also may display data received from devices connected to the modular control tower in conjunction with images and overlaid images.

FIG. 10 illustrates a surgical hub 206 comprising a plurality of modules coupled to the modular control tower 236. The modular control tower 236 comprises a modular communication hub 203, e.g., a network connectivity device, and a computer system 210 to provide local processing, visualization, and imaging, for example. As shown in FIG. 10, the modular communication hub 203 may be connected in a tiered configuration to expand the number of modules (e.g., devices) that may be connected to the modular communication hub 203 and transfer data associated with the modules to the computer system 210, cloud computing resources, or both. As shown in FIG. 10, each of the network hubs/switches in the modular communication hub 203 includes three downstream ports and one upstream port. The upstream network hub/switch is connected to a processor to provide a communication connection to the cloud computing resources and a local display 217. Communication to the cloud 204 may be made either through a wired or a wireless communication channel.

The surgical hub 206 employs a non-contact sensor module 242 to measure the dimensions of the operating theater and generate a map of the surgical theater using either ultrasonic or laser-type non-contact measurement devices. An ultrasound-based non-contact sensor module scans the operating theater by transmitting a burst of ultrasound and receiving the echo when it bounces off the perimeter walls of an operating theater as described under the heading “Surgical Hub Spatial Awareness Within an Operating Room” in U.S. Provisional Patent Application Ser. No. 62/611,341, titled INTERACTIVE SURGICAL PLATFORM, filed Dec. 28, 2017, which is herein incorporated by reference in its entirety, in which the sensor module is configured to determine the size of the operating theater and to adjust Bluetooth-pairing distance limits. A laser-based non-contact sensor module scans the operating theater by transmitting laser light pulses, receiving laser light pulses that bounce off the perimeter walls of the operating theater, and comparing the phase of the transmitted pulse to the received pulse to determine the size of the operating theater and to adjust Bluetooth pairing distance limits, for example.

The computer system 210 comprises a processor 244 and a network interface 245. The processor 244 is coupled to a communication module 247, storage 248, memory 249, non-volatile memory 250, and input/output interface 251 via a system bus. The system bus can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, 9-bit bus, Industrial Standard Architecture (ISA), Micro-Charmel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), USB, Advanced Graphics Port (AGP), Personal Computer Memory Card International Association bus (PCMCIA), Small Computer Systems Interface (SCSI), or any other proprietary bus.

The processor 244 may be any single-core or multicore processor such as those known under the trade name ARM Cortex by Texas Instruments. In one aspect, the processor may be an LM4F230H5QR ARM Cortex-M4F Processor Core, available from Texas Instruments, for example, comprising an on-chip memory of 256 KB single-cycle flash memory, or other non-volatile memory, up to 40 MHz, a prefetch buffer to improve performance above 40 MHz, a 32 KB single-cycle serial random access memory (SRAM), an internal read-only memory (ROM) loaded with StellarisWare® software, a 2 KB electrically erasable programmable read-only memory (EEPROM), and/or one or more pulse width modulation (PWM) modules, one or more quadrature encoder inputs (QEI) analogs, one or more 12-bit analog-to-digital converters (ADCs) with 12 analog input channels, details of which are available for the product datasheet.

In one aspect, the processor 244 may comprise a safety controller comprising two controller-based families such as TMS570 and RM4x, known under the trade name Hercules ARM Cortex R4, also by Texas Instruments. The safety controller may be configured specifically for IEC 61508 and ISO 26262 safety critical applications, among others, to provide advanced integrated safety features while delivering scalable performance, connectivity, and memory options.

The system memory includes volatile memory and non-volatile memory. The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer system, such as during start-up, is stored in non-volatile memory. For example, the non-volatile memory can include ROM, programmable ROM (PROM), electrically programmable ROM (EPROM), EEPROM, or flash memory. Volatile memory includes random-access memory (RAM), which acts as external cache memory. Moreover, RAM is available in many forms such as SRAM, dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).

The computer system 210 also includes removable/non-removable, volatile/non-volatile computer storage media, such as for example disk storage. The disk storage includes, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-60 drive, flash memory card, or memory stick. In addition, the disk storage can include storage media separately or in combination with other storage media including, but not limited to, an optical disc drive such as a compact disc ROM device (CD-ROM), compact disc recordable drive (CD-R Drive), compact disc rewritable drive (CD-RW Drive), or a digital versatile disc ROM drive (DVD-ROM). To facilitate the connection of the disk storage devices to the system bus, a removable or non-removable interface may be employed.

It is to be appreciated that the computer system 210 includes software that acts as an intermediary between users and the basic computer resources described in a suitable operating environment. Such software includes an operating system. The operating system, which can be stored on the disk storage, acts to control and allocate resources of the computer system. System applications take advantage of the management of resources by the operating system through program modules and program data stored either in the system memory or on the disk storage. It is to be appreciated that various components described herein can be implemented with various operating systems or combinations of operating systems.

A user enters commands or information into the computer system 210 through input device(s) coupled to the I/O interface 251. The input devices include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices connect to the processor through the system bus via interface port(s). The interface port(s) include, for example, a serial port, a parallel port, a game port, and a USB. The output device(s) use some of the same types of ports as input device(s). Thus, for example, a USB port may be used to provide input to the computer system and to output information from the computer system to an output device. An output adapter is provided to illustrate that there are some output devices like monitors, displays, speakers, and printers, among other output devices that require special adapters. The output adapters include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device and the system bus. It should be noted that other devices and/or systems of devices, such as remote computer(s), provide both input and output capabilities.

The computer system 210 can operate in a networked environment using logical connections to one or more remote computers, such as cloud computer(s), or local computers. The remote cloud computer(s) can be a personal computer, server, router, network PC, workstation, microprocessor-based appliance, peer device, or other common network node, and the like, and typically includes many or all of the elements described relative to the computer system. For purposes of brevity, only a memory storage device is illustrated with the remote computer(s). The remote computer(s) is logically connected to the computer system through a network interface and then physically connected via a communication connection. The network interface encompasses communication networks such as local area networks (LANs) and wide area networks (WANs). LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and the like. WAN technologies include, but are not limited to, point-to-point links, circuit-switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet-switching networks, and Digital Subscriber Lines (DSL).

In various aspects, the computer system 210 of FIG. 10, the imaging module 238 and/or visualization system 208, and/or the processor module 232 of FIGS. 9-10, may comprise an image processor, image-processing engine, media processor, or any specialized digital signal processor (DSP) used for the processing of digital images. The image processor may employ parallel computing with single instruction, multiple data (SIMD) or multiple instruction, multiple data (MIMD) technologies to increase speed and efficiency. The digital image-processing engine can perform a range of tasks. The image processor may be a system on a chip with multicore processor architecture.

The communication connection(s) refers to the hardware/software employed to connect the network interface to the bus. While the communication connection is shown for illustrative clarity inside the computer system, it can also be external to the computer system 210. The hardware/software necessary for connection to the network interface includes, for illustrative purposes only, internal and external technologies such as modems, including regular telephone-grade modems, cable modems, and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 11 illustrates a functional block diagram of one aspect of a USB network hub 300 device, in accordance with at least one aspect of the present disclosure. In the illustrated aspect, the USB network hub device 300 employs a TUSB2036 integrated circuit hub by Texas Instruments. The USB network hub 300 is a CMOS device that provides an upstream USB transceiver port 302 and up to three downstream USB transceiver ports 304, 306, 308 in compliance with the USB 2.0 specification. The upstream USB transceiver port 302 is a differential root data port comprising a differential data minus (DM0) input paired with a differential data plus (DP0) input. The three downstream USB transceiver ports 304, 306, 308 are differential data ports where each port includes differential data plus (DP1-DP3) outputs paired with differential data minus (DM1-DM3) outputs.

The USB network hub 300 device is implemented with a digital state machine instead of a microcontroller, and no firmware programming is required. Fully compliant USB transceivers are integrated into the circuit for the upstream USB transceiver port 302 and all downstream USB transceiver ports 304, 306, 308. The downstream USB transceiver ports 304, 306, 308 support both full-speed and low-speed devices by automatically setting the slew rate according to the speed of the device attached to the ports. The USB network hub 300 device may be configured either in bus-powered or self-powered mode and includes a hub power logic 312 to manage power.

The USB network hub 300 device includes a serial interface engine 310 (SIE). The SIE 310 is the front end of the USB network hub 300 hardware and handles most of the protocol described in chapter 8 of the USB specification. The SIE 310 typically comprehends signaling up to the transaction level. The functions that it handles could include: packet recognition, transaction sequencing, SOP, EOP, RESET, and RESUME signal detection/generation, clock/data separation, non-return-to-zero invert (NRZI) data encoding/decoding and bit-stuffing, CRC generation and checking (token and data), packet ID (PID) generation and checking/decoding, and/or serial-parallel/parallel-serial conversion. The 310 receives a clock input 314 and is coupled to a suspend/resume logic and frame timer 316 circuit and a hub repeater circuit 318 to control communication between the upstream USB transceiver port 302 and the downstream USB transceiver ports 304, 306, 308 through port logic circuits 320, 322, 324. The SIE 310 is coupled to a command decoder 326 via interface logic 328 to control commands from a serial EEPROM via a serial EEPROM interface 330.

In various aspects, the USB network hub 300 can connect 127 functions configured in up to six logical layers (tiers) to a single computer. Further, the USB network hub 300 can connect to all peripherals using a standardized four-wire cable that provides both communication and power distribution. The power configurations are bus-powered and self-powered modes. The USB network hub 300 may be configured to support four modes of power management: a bus-powered hub, with either individual-port power management or ganged-port power management, and the self-powered hub, with either individual-port power management or ganged-port power management. In one aspect, using a USB cable, the USB network hub 300, the upstream USB transceiver port 302 is plugged into a USB host controller, and the downstream USB transceiver ports 304, 306, 308 are exposed for connecting USB compatible devices, and so forth.

Additional details regarding the structure and function of the surgical hub and/or surgical hub networks can be found in U.S. Provisional Patent Application No. 62/659,900, titled METHOD OF HUB COMMUNICATION, filed Apr. 19, 2018, which is hereby incorporated by reference herein in its entirety.

Cloud System Hardware and Functional Modules

FIG. 12 is a block diagram of the computer-implemented interactive surgical system, in accordance with at least one aspect of the present disclosure. In one aspect, the computer-implemented interactive surgical system is configured to monitor and analyze data related to the operation of various surgical systems that include surgical hubs, surgical instruments, robotic devices and operating theaters or healthcare facilities. The computer-implemented interactive surgical system comprises a cloud-based analytics system. Although the cloud-based analytics system is described as a surgical system, it is not necessarily limited as such and could be a cloud-based medical system generally. As illustrated in FIG. 12, the cloud-based analytics system comprises a plurality of surgical instruments 7012 (may be the same or similar to instruments 112), a plurality of surgical hubs 7006 (may be the same or similar to hubs 106), and a surgical data network 7001 (may be the same or similar to network 201) to couple the surgical hubs 7006 to the cloud 7004 (may be the same or similar to cloud 204). Each of the plurality of surgical hubs 7006 is communicatively coupled to one or more surgical instruments 7012. The hubs 7006 are also communicatively coupled to the cloud 7004 of the computer-implemented interactive surgical system via the network 7001. The cloud 7004 is a remote centralized source of hardware and software for storing, manipulating, and communicating data generated based on the operation of various surgical systems. As shown in FIG. 12, access to the cloud 7004 is achieved via the network 7001, which may be the Internet or some other suitable computer network. Surgical hubs 7006 that are coupled to the cloud 7004 can be considered the client side of the cloud computing system (i.e., cloud-based analytics system). Surgical instruments 7012 are paired with the surgical hubs 7006 for control and implementation of various surgical procedures or operations as described herein.

In addition, surgical instruments 7012 may comprise transceivers for data transmission to and from their corresponding surgical hubs 7006 (which may also comprise transceivers). Combinations of surgical instruments 7012 and corresponding hubs 7006 may indicate particular locations, such as operating theaters in healthcare facilities (e.g., hospitals), for providing medical operations. For example, the memory of a surgical hub 7006 may store location data. As shown in FIG. 12, the cloud 7004 comprises central servers 7013 (which may be same or similar to remote server 113 in FIG. 1 and/or remote server 213 in FIG. 9), hub application servers 7002, data analytics modules 7034, and an input/output (“I/O”) interface 7007. The central servers 7013 of the cloud 7004 collectively administer the cloud computing system, which includes monitoring requests by client surgical hubs 7006 and managing the processing capacity of the cloud 7004 for executing the requests. Each of the central servers 7013 comprises one or more processors 7008 coupled to suitable memory devices 7010 which can include volatile memory such as random-access memory (RAM) and non-volatile memory such as magnetic storage devices. The memory devices 7010 may comprise machine executable instructions that when executed cause the processors 7008 to execute the data analytics modules 7034 for the cloud-based data analysis, operations, recommendations and other operations described below. Moreover, the processors 7008 can execute the data analytics modules 7034 independently or in conjunction with hub applications independently executed by the hubs 7006. The central servers 7013 also comprise aggregated medical data databases 2212, which can reside in the memory 2210.

Based on connections to various surgical hubs 7006 via the network 7001, the cloud 7004 can aggregate data from specific data generated by various surgical instruments 7012 and their corresponding hubs 7006. Such aggregated data may be stored within the aggregated medical databases 7011 of the cloud 7004. In particular, the cloud 7004 may advantageously perform data analysis and operations on the aggregated data to yield insights and/or perform functions that individual hubs 7006 could not achieve on their own. To this end, as shown in FIG. 12, the cloud 7004 and the surgical hubs 7006 are communicatively coupled to transmit and receive information. The I/O interface 7007 is connected to the plurality of surgical hubs 7006 via the network 7001. In this way, the I/O interface 7007 can be configured to transfer information between the surgical hubs 7006 and the aggregated medical data databases 7011. Accordingly, the I/O interface 7007 may facilitate read/write operations of the cloud-based analytics system. Such read/write operations may be executed in response to requests from hubs 7006. These requests could be transmitted to the hubs 7006 through the hub applications. The I/O interface 7007 may include one or more high speed data ports, which may include universal serial bus (USB) ports, IEEE 1394 ports, as well as W-Fi and Bluetooth I/O interfaces for connecting the cloud 7004 to hubs 7006. The hub application servers 7002 of the cloud 7004 are configured to host and supply shared capabilities to software applications (e.g. hub applications) executed by surgical hubs 7006. For example, the hub application servers 7002 may manage requests made by the hub applications through the hubs 7006, control access to the aggregated medical data databases 7011, and perform load balancing. The data analytics modules 7034 are described in further detail with reference to FIG. 13.

The particular cloud computing system configuration described in the present disclosure is specifically designed to address various issues arising in the context of medical operations and procedures performed using medical devices, such as the surgical instruments 7012, 112. In particular, the surgical instruments 7012 may be digital surgical devices configured to interact with the cloud 7004 for implementing techniques to improve the performance of surgical operations. Various surgical instruments 7012 and/or surgical hubs 7006 may comprise touch controlled user interfaces such that clinicians may control aspects of interaction between the surgical instruments 7012 and the cloud 7004. Other suitable user interfaces for control such as auditory controlled user interfaces can also be used.

FIG. 13 is a block diagram which illustrates the functional architecture of the computer-implemented interactive surgical system, in accordance with at least one aspect of the present disclosure. The cloud-based analytics system includes a plurality of data analytics modules 7034 that may be executed by the processors 7008 of the cloud 7004 for providing data analytic solutions to problems specifically arising in the medical field. As shown in FIG. 13, the functions of the cloud-based data analytics modules 7034 may be assisted via hub applications 7014 hosted by the hub application servers 7002 that may be accessed on surgical hubs 7006. The cloud processors 7008 and hub applications 7014 may operate in conjunction to execute the data analytics modules 7034. Application program interfaces (APIs) 7016 define the set of protocols and routines corresponding to the hub applications 7014. Additionally, the APIs 7016 manage the storing and retrieval of data into and from the aggregated medical data databases 7011 for the operations of the applications 7014. The caches 7018 also store data (e.g., temporarily) and are coupled to the APIs 7016 for more efficient retrieval of data used by the applications 7014. The data analytics modules 7034 in FIG. 13 include modules for resource optimization 7020, data collection and aggregation 7022, authorization and security 7024, control program updating 7026, patient outcome analysis 7028, recommendations 7030, and data sorting and prioritization 7032. Other suitable data analytics modules could also be implemented by the cloud 7004, according to some aspects. In one aspect, the data analytics modules are used for specific recommendations based on analyzing trends, outcomes, and other data.

For example, the data collection and aggregation module 7022 could be used to generate self-describing data (e.g., metadata) including identification of notable features or configuration (e.g., trends), management of redundant data sets, and storage of the data in paired data sets which can be grouped by surgery but not necessarily keyed to actual surgical dates and surgeons. In particular, pair data sets generated from operations of surgical instruments 7012 can comprise applying a binary classification, e.g., a bleeding or a non-bleeding event. More generally, the binary classification may be characterized as either a desirable event (e.g., a successful surgical procedure) or an undesirable event (e.g., a misfired or misused surgical instrument 7012). The aggregated self-describing data may correspond to individual data received from various groups or subgroups of surgical hubs 7006. Accordingly, the data collection and aggregation module 7022 can generate aggregated metadata or other organized data based on raw data received from the surgical hubs 7006. To this end, the processors 7008 can be operationally coupled to the hub applications 7014 and aggregated medical data databases 7011 for executing the data analytics modules 7034. The data collection and aggregation module 7022 may store the aggregated organized data into the aggregated medical data databases 2212.

The resource optimization module 7020 can be configured to analyze this aggregated data to determine an optimal usage of resources for a particular or group of healthcare facilities. For example, the resource optimization module 7020 may determine an optimal order point of surgical stapling instruments 7012 for a group of healthcare facilities based on corresponding predicted demand of such instruments 7012. The resource optimization module 7020 might also assess the resource usage or other operational configurations of various healthcare facilities to determine whether resource usage could be improved. Similarly, the recommendations module 7030 can be configured to analyze aggregated organized data from the data collection and aggregation module 7022 to provide recommendations. For example, the recommendations module 7030 could recommend to healthcare facilities (e.g., medical service providers such as hospitals) that a particular surgical instrument 7012 should be upgraded to an improved version based on a higher than expected error rate, for example. Additionally, the recommendations module 7030 and/or resource optimization module 7020 could recommend better supply chain parameters such as product reorder points and provide suggestions of different surgical instrument 7012, uses thereof, or procedure steps to improve surgical outcomes. The healthcare facilities can receive such recommendations via corresponding surgical hubs 7006. More specific recommendations regarding parameters or configurations of various surgical instruments 7012 can also be provided. Hubs 7006 and/or surgical instruments 7012 each could also have display screens that display data or recommendations provided by the cloud 7004.

The patient outcome analysis module 7028 can analyze surgical outcomes associated with currently used operational parameters of surgical instruments 7012. The patient outcome analysis module 7028 may also analyze and assess other potential operational parameters. In this connection, the recommendations module 7030 could recommend using these other potential operational parameters based on yielding better surgical outcomes, such as better sealing or less bleeding. For example, the recommendations module 7030 could transmit recommendations to a surgical hub 7006 regarding when to use a particular cartridge for a corresponding stapling surgical instrument 7012. Thus, the cloud-based analytics system, while controlling for common variables, may be configured to analyze the large collection of raw data and to provide centralized recommendations over multiple healthcare facilities (advantageously determined based on aggregated data). For example, the cloud-based analytics system could analyze, evaluate, and/or aggregate data based on type of medical practice, type of patient, number of patients, geographic similarity between medical providers, which medical providers/facilities use similar types of instruments, etc., in a way that no single healthcare facility alone would be able to analyze independently.

The control program updating module 7026 could be configured to implement various surgical instrument 7012 recommendations when corresponding control programs are updated. For example, the patient outcome analysis module 7028 could identify correlations linking specific control parameters with successful (or unsuccessful) results. Such correlations may be addressed when updated control programs are transmitted to surgical instruments 7012 via the control program updating module 7026. Updates to instruments 7012 that are transmitted via a corresponding hub 7006 may incorporate aggregated performance data that was gathered and analyzed by the data collection and aggregation module 7022 of the cloud 7004. Additionally, the patient outcome analysis module 7028 and recommendations module 7030 could identify improved methods of using instruments 7012 based on aggregated performance data.

The cloud-based analytics system may include security features implemented by the cloud 7004. These security features may be managed by the authorization and security module 7024. Each surgical hub 7006 can have associated unique credentials such as username, password, and other suitable security credentials. These credentials could be stored in the memory 7010 and be associated with a permitted cloud access level. For example, based on providing accurate credentials, a surgical hub 7006 may be granted access to communicate with the cloud to a predetermined extent (e.g., may only engage in transmitting or receiving certain defined types of information). To this end, the aggregated medical data databases 7011 of the cloud 7004 may comprise a database of authorized credentials for verifying the accuracy of provided credentials. Different credentials may be associated with varying levels of permission for interaction with the cloud 7004, such as a predetermined access level for receiving the data analytics generated by the cloud 7004.

Furthermore, for security purposes, the cloud could maintain a database of hubs 7006, instruments 7012, and other devices that may comprise a “black list” of prohibited devices. In particular, a surgical hub 7006 listed on the black list may not be permitted to interact with the cloud, while surgical instruments 7012 listed on the black list may not have functional access to a corresponding hub 7006 and/or may be prevented from fully functioning when paired to its corresponding hub 7006. Additionally or alternatively, the cloud 7004 may flag instruments 7012 based on incompatibility or other specified criteria. In this manner, counterfeit medical devices and improper reuse of such devices throughout the cloud-based analytics system can be identified and addressed.

The surgical instruments 7012 may use wireless transceivers to transmit wireless signals that may represent, for example, authorization credentials for access to corresponding hubs 7006 and the cloud 7004. Wired transceivers may also be used to transmit signals. Such authorization credentials can be stored in the respective memory devices of the surgical instruments 7012. The authorization and security module 7024 can determine whether the authorization credentials are accurate or counterfeit. The authorization and security module 7024 may also dynamically generate authorization credentials for enhanced security. The credentials could also be encrypted, such as by using hash based encryption. Upon transmitting proper authorization, the surgical instruments 7012 may transmit a signal to the corresponding hubs 7006 and ultimately the cloud 7004 to indicate that the instruments 7012 are ready to obtain and transmit medical data. In response, the cloud 7004 may transition into a state enabled for receiving medical data for storage into the aggregated medical data databases 7011. This data transmission readiness could be indicated by a light indicator on the instruments 7012, for example. The cloud 7004 can also transmit signals to surgical instruments 7012 for updating their associated control programs. The cloud 7004 can transmit signals that are directed to a particular class of surgical instruments 7012 (e.g., electrosurgical instruments) so that software updates to control programs are only transmitted to the appropriate surgical instruments 7012. Moreover, the cloud 7004 could be used to implement system wide solutions to address local or global problems based on selective data transmission and authorization credentials. For example, if a group of surgical instruments 7012 are identified as having a common manufacturing defect, the cloud 7004 may change the authorization credentials corresponding to this group to implement an operational lockout of the group.

The cloud-based analytics system may allow for monitoring multiple healthcare facilities (e.g., medical facilities like hospitals) to determine improved practices and recommend changes (via the recommendations module 2030, for example) accordingly. Thus, the processors 7008 of the cloud 7004 can analyze data associated with an individual healthcare facility to identify the facility and aggregate the data with other data associated with other healthcare facilities in a group. Groups could be defined based on similar operating practices or geographical location, for example. In this way, the cloud 7004 may provide healthcare facility group wide analysis and recommendations. The cloud-based analytics system could also be used for enhanced situational awareness. For example, the processors 7008 may predictively model the effects of recommendations on the cost and effectiveness for a particular facility (relative to overall operations and/or various medical procedures). The cost and effectiveness associated with that particular facility can also be compared to a corresponding local region of other facilities or any other comparable facilities.

The data sorting and prioritization module 7032 may prioritize and sort data based on criticality (e.g., the severity of a medical event associated with the data, unexpectedness, suspiciousness). This sorting and prioritization may be used in conjunction with the functions of the other data analytics modules 7034 described above to improve the cloud-based analytics and operations described herein. For example, the data sorting and prioritization module 7032 can assign a priority to the data analysis performed by the data collection and aggregation module 7022 and patient outcome analysis modules 7028. Different prioritization levels can result in particular responses from the cloud 7004 (corresponding to a level of urgency) such as escalation for an expedited response, special processing, exclusion from the aggregated medical data databases 7011, or other suitable responses. Moreover, if necessary, the cloud 7004 can transmit a request (e.g. a push message) through the hub application servers for additional data from corresponding surgical instruments 7012. The push message can result in a notification displayed on the corresponding hubs 7006 for requesting supporting or additional data. This push message may be required in situations in which the cloud detects a significant irregularity or outlier and the cloud cannot determine the cause of the irregularity. The central servers 7013 may be programmed to trigger this push message in certain significant circumstances, such as when data is determined to be different from an expected value beyond a predetermined threshold or when it appears security has been comprised, for example.

Additional details regarding the cloud analysis system can be found in U.S. Provisional Patent Application No. 62/659,900, titled METHOD OF HUB COMMUNICATION, filed Apr. 19, 2018, which is hereby incorporated by reference herein in its entirety.

Situational Awareness

Although an “intelligent” device including control algorithms that respond to sensed data can be an improvement over a “dumb” device that operates without accounting for sensed data, some sensed data can be incomplete or inconclusive when considered in isolation, i.e., without the context of the type of surgical procedure being performed or the type of tissue that is being operated on. Without knowing the procedural context (e.g., knowing the type of tissue being operated on or the type of procedure being performed), the control algorithm may control the modular device incorrectly or suboptimally given the particular context-free sensed data. For example, the optimal manner for a control algorithm to control a surgical instrument in response to a particular sensed parameter can vary according to the particular tissue type being operated on. This is due to the fact that different tissue types have different properties (e.g., resistance to tearing) and thus respond differently to actions taken by surgical instruments. Therefore, it may be desirable for a surgical instrument to take different actions even when the same measurement for a particular parameter is sensed. As one specific example, the optimal manner in which to control a surgical stapling and cutting instrument in response to the instrument sensing an unexpectedly high force to close its end effector will vary depending upon whether the tissue type is susceptible or resistant to tearing. For tissues that are susceptible to tearing, such as lung tissue, the instrument's control algorithm would optimally ramp down the motor in response to an unexpectedly high force to close to avoid tearing the tissue. For tissues that are resistant to tearing, such as stomach tissue, the instrument's control algorithm would optimally ramp up the motor in response to an unexpectedly high force to close to ensure that the end effector is clamped properly on the tissue. Without knowing whether lung or stomach tissue has been clamped, the control algorithm may make a suboptimal decision.

One solution utilizes a surgical hub including a system that is configured to derive information about the surgical procedure being performed based on data received from various data sources and then control the paired modular devices accordingly. In other words, the surgical hub is configured to infer information about the surgical procedure from received data and then control the modular devices paired to the surgical hub based upon the inferred context of the surgical procedure. FIG. 14 illustrates a diagram of a situationally aware surgical system 5100, in accordance with at least one aspect of the present disclosure. In some exemplifications, the data sources 5126 include, for example, the modular devices 5102 (which can include sensors configured to detect parameters associated with the patient and/or the modular device itself), databases 5122 (e.g., an EMR database containing patient records), and patient monitoring devices 5124 (e.g., a blood pressure (BP) monitor and an electrocardiography (EKG) monitor).

A surgical hub 5104, which may be similar to the hub 106 in many respects, can be configured to derive the contextual information pertaining to the surgical procedure from the data based upon, for example, the particular combination(s) of received data or the particular order in which the data is received from the data sources 5126. The contextual information inferred from the received data can include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure that the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure. This ability by some aspects of the surgical hub 5104 to derive or infer information related to the surgical procedure from received data can be referred to as “situational awareness.” In one exemplification, the surgical hub 5104 can incorporate a situational awareness system, which is the hardware and/or programming associated with the surgical hub 5104 that derives contextual information pertaining to the surgical procedure from the received data.

The situational awareness system of the surgical hub 5104 can be configured to derive the contextual information from the data received from the data sources 5126 in a variety of different ways. In one exemplification, the situational awareness system includes a pattern recognition system, or machine learning system (e.g., an artificial neural network), that has been trained on training data to correlate various inputs (e.g., data from databases 5122, patient monitoring devices 5124, and/or modular devices 5102) to corresponding contextual information regarding a surgical procedure. In other words, a machine learning system can be trained to accurately derive contextual information regarding a surgical procedure from the provided inputs. In another exemplification, the situational awareness system can include a lookup table storing pre-characterized contextual information regarding a surgical procedure in association with one or more inputs (or ranges of inputs) corresponding to the contextual information. In response to a query with one or more inputs, the lookup table can return the corresponding contextual information for the situational awareness system for controlling the modular devices 5102. In one exemplification, the contextual information received by the situational awareness system of the surgical hub 5104 is associated with a particular control adjustment or set of control adjustments for one or more modular devices 5102. In another exemplification, the situational awareness system includes a further machine learning system, lookup table, or other such system, which generates or retrieves one or more control adjustments for one or more modular devices 5102 when provided the contextual information as input.

A surgical hub 5104 incorporating a situational awareness system provides a number of benefits for the surgical system 5100. One benefit includes improving the interpretation of sensed and collected data, which would in turn improve the processing accuracy and/or the usage of the data during the course of a surgical procedure. To return to a previous example, a situationally aware surgical hub 5104 could determine what type of tissue was being operated on; therefore, when an unexpectedly high force to close the surgical instrument's end effector is detected, the situationally aware surgical hub 5104 could correctly ramp up or ramp down the motor of the surgical instrument for the type of tissue.

As another example, the type of tissue being operated can affect the adjustments that are made to the compression rate and load thresholds of a surgical stapling and cutting instrument for a particular tissue gap measurement. A situationally aware surgical hub 5104 could infer whether a surgical procedure being performed is a thoracic or an abdominal procedure, allowing the surgical hub 5104 to determine whether the tissue clamped by an end effector of the surgical stapling and cutting instrument is lung (for a thoracic procedure) or stomach (for an abdominal procedure) tissue. The surgical hub 5104 could then adjust the compression rate and load thresholds of the surgical stapling and cutting instrument appropriately for the type of tissue.

As yet another example, the type of body cavity being operated in during an insufflation procedure can affect the function of a smoke evacuator. A situationally aware surgical hub 5104 could determine whether the surgical site is under pressure (by determining that the surgical procedure is utilizing insufflation) and determine the procedure type. As a procedure type is generally performed in a specific body cavity, the surgical hub 5104 could then control the motor rate of the smoke evacuator appropriately for the body cavity being operated in. Thus, a situationally aware surgical hub 5104 could provide a consistent amount of smoke evacuation for both thoracic and abdominal procedures.

As yet another example, the type of procedure being performed can affect the optimal energy level for an ultrasonic surgical instrument or radio frequency (RF) electrosurgical instrument to operate at. Arthroscopic procedures, for example, require higher energy levels because the end effector of the ultrasonic surgical instrument or RF electrosurgical instrument is immersed in fluid. A situationally aware surgical hub 5104 could determine whether the surgical procedure is an arthroscopic procedure. The surgical hub 5104 could then adjust the RF power level or the ultrasonic amplitude of the generator (i.e., “energy level”) to compensate for the fluid filled environment. Relatedly, the type of tissue being operated on can affect the optimal energy level for an ultrasonic surgical instrument or RF electrosurgical instrument to operate at. A situationally aware surgical hub 5104 could determine what type of surgical procedure is being performed and then customize the energy level for the ultrasonic surgical instrument or RF electrosurgical instrument, respectively, according to the expected tissue profile for the surgical procedure. Furthermore, a situationally aware surgical hub 5104 can be configured to adjust the energy level for the ultrasonic surgical instrument or RF electrosurgical instrument throughout the course of a surgical procedure, rather than just on a procedure-by-procedure basis. A situationally aware surgical hub 5104 could determine what step of the surgical procedure is being performed or will subsequently be performed and then update the control algorithms for the generator and/or ultrasonic surgical instrument or RF electrosurgical instrument to set the energy level at a value appropriate for the expected tissue type according to the surgical procedure step.

As yet another example, data can be drawn from additional data sources 5126 to improve the conclusions that the surgical hub 5104 draws from one data source 5126. A situationally aware surgical hub 5104 could augment data that it receives from the modular devices 5102 with contextual information that it has built up regarding the surgical procedure from other data sources 5126. For example, a situationally aware surgical hub 5104 can be configured to determine whether hemostasis has occurred (i.e., whether bleeding at a surgical site has stopped) according to video or image data received from a medical imaging device. However, in some cases the video or image data can be inconclusive. Therefore, in one exemplification, the surgical hub 5104 can be further configured to compare a physiologic measurement (e.g., blood pressure sensed by a BP monitor communicably connected to the surgical hub 5104) with the visual or image data of hemostasis (e.g., from a medical imaging device 124 (FIG. 2) communicably coupled to the surgical hub 5104) to make a determination on the integrity of the staple line or tissue weld. In other words, the situational awareness system of the surgical hub 5104 can consider the physiological measurement data to provide additional context in analyzing the visualization data. The additional context can be useful when the visualization data may be inconclusive or incomplete on its own.

Another benefit includes proactively and automatically controlling the paired modular devices 5102 according to the particular step of the surgical procedure that is being performed to reduce the number of times that medical personnel are required to interact with or control the surgical system 5100 during the course of a surgical procedure. For example, a situationally aware surgical hub 5104 could proactively activate the generator to which an RF electrosurgical instrument is connected if it determines that a subsequent step of the procedure requires the use of the instrument. Proactively activating the energy source allows the instrument to be ready for use a soon as the preceding step of the procedure is completed.

As another example, a situationally aware surgical hub 5104 could determine whether the current or subsequent step of the surgical procedure requires a different view or degree of magnification on the display according to the feature(s) at the surgical site that the surgeon is expected to need to view. The surgical hub 5104 could then proactively change the displayed view (supplied by, e.g., a medical imaging device for the visualization system 108) accordingly so that the display automatically adjusts throughout the surgical procedure.

As yet another example, a situationally aware surgical hub 5104 could determine which step of the surgical procedure is being performed or will subsequently be performed and whether particular data or comparisons between data will be required for that step of the surgical procedure. The surgical hub 5104 can be configured to automatically call up data screens based upon the step of the surgical procedure being performed, without waiting for the surgeon to ask for the particular information.

Another benefit includes checking for errors during the setup of the surgical procedure or during the course of the surgical procedure. For example, a situationally aware surgical hub 5104 could determine whether the operating theater is setup properly or optimally for the surgical procedure to be performed. The surgical hub 5104 can be configured to determine the type of surgical procedure being performed, retrieve the corresponding checklists, product location, or setup needs (e.g., from a memory), and then compare the current operating theater layout to the standard layout for the type of surgical procedure that the surgical hub 5104 determines is being performed. In one exemplification, the surgical hub 5104 can be configured to compare the list of items for the procedure scanned by a suitable scanner, for example, and/or a list of devices paired with the surgical hub 5104 to a recommended or anticipated manifest of items and/or devices for the given surgical procedure. If there are any discontinuities between the lists, the surgical hub 5104 can be configured to provide an alert indicating that a particular modular device 5102, patient monitoring device 5124, and/or other surgical item is missing. In one exemplification, the surgical hub 5104 can be configured to determine the relative distance or position of the modular devices 5102 and patient monitoring devices 5124 via proximity sensors, for example. The surgical hub 5104 can compare the relative positions of the devices to a recommended or anticipated layout for the particular surgical procedure. If there are any discontinuities between the layouts, the surgical hub 5104 can be configured to provide an alert indicating that the current layout for the surgical procedure deviates from the recommended layout.

As another example, a situationally aware surgical hub 5104 could determine whether the surgeon (or other medical personnel) was making an error or otherwise deviating from the expected course of action during the course of a surgical procedure. For example, the surgical hub 5104 can be configured to determine the type of surgical procedure being performed, retrieve the corresponding list of steps or order of equipment usage (e.g., from a memory), and then compare the steps being performed or the equipment being used during the course of the surgical procedure to the expected steps or equipment for the type of surgical procedure that the surgical hub 5104 determined is being performed. In one exemplification, the surgical hub 5104 can be configured to provide an alert indicating that an unexpected action is being performed or an unexpected device is being utilized at the particular step in the surgical procedure.

Overall, the situational awareness system for the surgical hub 5104 improves surgical procedure outcomes by adjusting the surgical instruments (and other modular devices 5102) for the particular context of each surgical procedure (such as adjusting to different tissue types) and validating actions during a surgical procedure. The situational awareness system also improves surgeons' efficiency in performing surgical procedures by automatically suggesting next steps, providing data, and adjusting displays and other modular devices 5102 in the surgical theater according to the specific context of the procedure.

Referring now to FIG. 15, a timeline 5200 depicting situational awareness of a hub, such as the surgical hub 106 or 206 (FIGS. 1-11), for example, is depicted. The timeline 5200 is an illustrative surgical procedure and the contextual information that the surgical hub 106, 206 can derive from the data received from the data sources at each step in the surgical procedure. The timeline 5200 depicts the typical steps that would be taken by the nurses, surgeons, and other medical personnel during the course of a lung segmentectomy procedure, beginning with setting up the operating theater and ending with transferring the patient to a post-operative recovery room.

The situationally aware surgical hub 106, 206 receives data from the data sources throughout the course of the surgical procedure, including data generated each time medical personnel utilize a modular device that is paired with the surgical hub 106, 206. The surgical hub 106, 206 can receive this data from the paired modular devices and other data sources and continually derive inferences (i.e., contextual information) about the ongoing procedure as new data is received, such as which step of the procedure is being performed at any given time. The situational awareness system of the surgical hub 106, 206 is able to, for example, record data pertaining to the procedure for generating reports, verify the steps being taken by the medical personnel, provide data or prompts (e.g., via a display screen) that may be pertinent for the particular procedural step, adjust modular devices based on the context (e.g., activate monitors, adjust the field of view (FOV) of the medical imaging device, or change the energy level of an ultrasonic surgical instrument or RF electrosurgical instrument), and take any other such action described above.

As the first step S202 in this illustrative procedure, the hospital staff members retrieve the patient's EMR from the hospital's EMR database. Based on select patient data in the EMR, the surgical hub 106, 206 determines that the procedure to be performed is a thoracic procedure.

Second step S204, the staff members scan the incoming medical supplies for the procedure. The surgical hub 106, 206 cross-references the scanned supplies with a list of supplies that are utilized in various types of procedures and confirms that the mix of supplies corresponds to a thoracic procedure. Further, the surgical hub 106, 206 is also able to determine that the procedure is not a wedge procedure (because the incoming supplies either lack certain supplies that are necessary for a thoracic wedge procedure or do not otherwise correspond to a thoracic wedge procedure).

Third step S206, the medical personnel scan the patient band via a scanner that is communicably connected to the surgical hub 106, 206. The surgical hub 106, 206 can then confirm the patient's identity based on the scanned data.

Fourth step S208, the medical staff turns on the auxiliary equipment. The auxiliary equipment being utilized can vary according to the type of surgical procedure and the techniques to be used by the surgeon, but in this illustrative case they include a smoke evacuator, insufflator, and medical imaging device. When activated, the auxiliary equipment that are modular devices can automatically pair with the surgical hub 106, 206 that is located within a particular vicinity of the modular devices as part of their initialization process. The surgical hub 106, 206 can then derive contextual information about the surgical procedure by detecting the types of modular devices that pair with it during this pre-operative or initialization phase. In this particular example, the surgical hub 106, 206 determines that the surgical procedure is a VATS procedure based on this particular combination of paired modular devices. Based on the combination of the data from the patient's EMR, the list of medical supplies to be used in the procedure, and the type of modular devices that connect to the hub, the surgical hub 106, 206 can generally infer the specific procedure that the surgical team will be performing. Once the surgical hub 106, 206 knows what specific procedure is being performed, the surgical hub 106, 206 can then retrieve the steps of that procedure from a memory or from the cloud and then cross-reference the data it subsequently receives from the connected data sources (e.g., modular devices and patient monitoring devices) to infer what step of the surgical procedure the surgical team is performing.

Fifth step S210, the staff members attach the EKG electrodes and other patient monitoring devices to the patient. The EKG electrodes and other patient monitoring devices are able to pair with the surgical hub 106, 206. As the surgical hub 106, 206 begins receiving data from the patient monitoring devices, the surgical hub 106, 206 thus confirms that the patient is in the operating theater.

Sixth step S212, the medical personnel induce anesthesia in the patient. The surgical hub 106, 206 can infer that the patient is under anesthesia based on data from the modular devices and/or patient monitoring devices, including EKG data, blood pressure data, ventilator data, or combinations thereof, for example. Upon completion of the sixth step S212, the pre-operative portion of the lung segmentectomy procedure is completed and the operative portion begins.

Seventh step S214, the patient's lung that is being operated on is collapsed (while ventilation is switched to the contralateral lung). The surgical hub 106, 206 can infer from the ventilator data that the patient's lung has been collapsed, for example. The surgical hub 106, 206 can infer that the operative portion of the procedure has commenced as it can compare the detection of the patient's lung collapsing to the expected steps of the procedure (which can be accessed or retrieved previously) and thereby determine that collapsing the lung is the first operative step in this particular procedure.

Eighth step S216, the medical imaging device (e.g., a scope) is inserted and video from the medical imaging device is initiated. The surgical hub 106, 206 receives the medical imaging device data (i.e., video or image data) through its connection to the medical imaging device. Upon receipt of the medical imaging device data, the surgical hub 106, 206 can determine that the laparoscopic portion of the surgical procedure has commenced. Further, the surgical hub 106, 206 can determine that the particular procedure being performed is a segmentectomy, as opposed to a lobectomy (note that a wedge procedure has already been discounted by the surgical hub 106, 206 based on data received at the second step S204 of the procedure). The data from the medical imaging device 124 (FIG. 2) can be utilized to determine contextual information regarding the type of procedure being performed in a number of different ways, including by determining the angle at which the medical imaging device is oriented with respect to the visualization of the patient's anatomy, monitoring the number or medical imaging devices being utilized (i.e., that are activated and paired with the surgical hub 106, 206), and monitoring the types of visualization devices utilized. For example, one technique for performing a VATS lobectomy places the camera in the lower anterior corner of the patient's chest cavity above the diaphragm, whereas one technique for performing a VATS segmentectomy places the camera in an anterior intercostal position relative to the segmental fissure. Using pattern recognition or machine learning techniques, for example, the situational awareness system can be trained to recognize the positioning of the medical imaging device according to the visualization of the patient's anatomy. As another example, one technique for performing a VATS lobectomy utilizes a single medical imaging device, whereas another technique for performing a VATS segmentectomy utilizes multiple cameras. As yet another example, one technique for performing a VATS segmentectomy utilizes an infrared light source (which can be communicably coupled to the surgical hub as part of the visualization system) to visualize the segmental fissure, which is not utilized in a VATS lobectomy. By tracking any or all of this data from the medical imaging device, the surgical hub 106, 206 can thereby determine the specific type of surgical procedure being performed and/or the technique being used for a particular type of surgical procedure.

Ninth step S218, the surgical team begins the dissection step of the procedure. The surgical hub 106, 206 can infer that the surgeon is in the process of dissecting to mobilize the patient's lung because it receives data from the RF or ultrasonic generator indicating that an energy instrument is being fired. The surgical hub 106, 206 can cross-reference the received data with the retrieved steps of the surgical procedure to determine that an energy instrument being fired at this point in the process (i.e., after the completion of the previously discussed steps of the procedure) corresponds to the dissection step. In certain instances, the energy instrument can be an energy tool mounted to a robotic arm of a robotic surgical system.

Tenth step S220, the surgical team proceeds to the ligation step of the procedure. The surgical hub 106, 206 can infer that the surgeon is ligating arteries and veins because it receives data from the surgical stapling and cutting instrument indicating that the instrument is being fired. Similarly to the prior step, the surgical hub 106, 206 can derive this inference by cross-referencing the receipt of data from the surgical stapling and cutting instrument with the retrieved steps in the process. In certain instances, the surgical instrument can be a surgical tool mounted to a robotic arm of a robotic surgical system.

Eleventh step S222, the segmentectomy portion of the procedure is performed. The surgical hub 106, 206 can infer that the surgeon is transecting the parenchyma based on data from the surgical stapling and cutting instrument, including data from its cartridge. The cartridge data can correspond to the size or type of staple being fired by the instrument, for example. As different types of staples are utilized for different types of tissues, the cartridge data can thus indicate the type of tissue being stapled and/or transected. In this case, the type of staple being fired is utilized for parenchyma (or other similar tissue types), which allows the surgical hub 106, 206 to infer that the segmentectomy portion of the procedure is being performed.

Twelfth step S224, the node dissection step is then performed. The surgical hub 106, 206 can infer that the surgical team is dissecting the node and performing a leak test based on data received from the generator indicating that an RF or ultrasonic instrument is being fired. For this particular procedure, an RF or ultrasonic instrument being utilized after parenchyma was transected corresponds to the node dissection step, which allows the surgical hub 106, 206 to make this inference. It should be noted that surgeons regularly switch back and forth between surgical stapling/cutting instruments and surgical energy (i.e., RF or ultrasonic) instruments depending upon the particular step in the procedure because different instruments are better adapted for particular tasks. Therefore, the particular sequence in which the stapling/cutting instruments and surgical energy instruments are used can indicate what step of the procedure the surgeon is performing. Moreover, in certain instances, robotic tools can be utilized for one or more steps in a surgical procedure and/or handheld surgical instruments can be utilized for one or more steps in the surgical procedure. The surgeon(s) can alternate between robotic tools and handheld surgical instruments and/or can use the devices concurrently, for example. Upon completion of the twelfth step S224, the incisions are closed up and the post-operative portion of the procedure begins.

Thirteenth step S226, the patient's anesthesia is reversed. The surgical hub 106, 206 can infer that the patient is emerging from the anesthesia based on the ventilator data (i.e., the patient's breathing rate begins increasing), for example.

Lastly, the fourteenth step S228 is that the medical personnel remove the various patient monitoring devices from the patient. The surgical hub 106, 206 can thus infer that the patient is being transferred to a recovery room when the hub loses EKG, BP, and other data from the patient monitoring devices. As can be seen from the description of this illustrative procedure, the surgical hub 106, 206 can determine or infer when each step of a given surgical procedure is taking place according to data received from the various data sources that are communicably coupled to the surgical hub 106, 206.

Situational awareness is further described in U.S. Provisional Patent Application Ser. No. 62/659,900, titled METHOD OF HUB COMMUNICATION, filed Apr. 19, 2018, which is herein incorporated by reference in its entirety. In certain instances, operation of a robotic surgical system, including the various robotic surgical systems disclosed herein, for example, can be controlled by the hub 106, 206 based on its situational awareness and/or feedback from the components thereof and/or based on information from the cloud 104.

Data Manipulation, Analysis, and Storage

In various aspects, a surgical hub system can be configured to collect rich contextual data pertaining to the use of surgical devices that are connected to the system, providing a hierarchy of awareness for the surgical hub system.

Data Indexing and Storage

Various techniques are described herein for data transformation, validation, organization, and fusion.

One issue that arises in the surgical hub system context is how to fuse data from diverse and different sources into a common data set that is useful. For example, what solutions are available to fuse two types of data that are recorded at different sampling rates? Can systems be made to be sampled at a similar rate? Can one insert a timing signal into all data to assist in synchronizing the data sets? The solution selected for various applications can depend on the particular types of surgical devices that are collecting the data sets being fused and other such factors.

In one aspect, the surgical hub can be configured to perform automated data scaling, alignment, and organizing of the collected data based on predefined parameters within the surgical hub prior to the data being transmitted. In one aspect, the predefined parameters could be established or altered by interaction between the surgical hub and the cloud system when configured for use. The cloud system may provide solutions from other hubs in the network that addressed a similar problem, and may also provide offline processing using various learning mechanisms to determine how to align the data. This enables the data collected by the surgical hub to be directly integrated into a larger cloud based database for analysis. In another aspect, the surgical hub and/or cloud system can be configured to change the sampling rate of measured systems. For example, the datasets could be organized into a functional database and/or analyzed via functional data analysis. For example, the system can be configured to include computational tabulation of multiple measures from the same or different devices into a single measure. For example, the system can be configured to include a hash function to encrypt or authenticate the source or sources of the data.

Data Wrangling

In one aspect, the surgical hub system can be configured to perform “data wrangling” or “munging,” i.e., the reorganization of raw data into a usable form. For example, the surgical hub system can be configured to organize data from the surgical hub and other equipment into a unified dataset.

Data Warehousing and Fusing

One challenge with integrating and fusing datasets are differing data rates, data configurations, file formats, and organizational methods of the data sources. Further, in moving the data into a single storage framework, it is useful to include the context of what were the conditions under which the data was recorded, any modification of the data into the expected format, and any calibration alteration of the data to make it what is directly comparable, as some examples.

FIG. 16 is a block diagram of a system for automated scaling, organizing, fusing and aligning of disparate data sets, in accordance with at least one aspect of the present disclosure. Illustration 204000 provides an example of how the data may be organized through several different stages. The automated system may be implemented in a medical hub that takes in data from multiple sources, such as one or more medical devices, enterprising software, and administrative software. In one aspect of the automated system, data can be extracted from a variety of data sources 204005 and placed in a staging module 204010. The sources may come from typical vendors and other proprietary data types from medical devices and other inputs that may be received at a medical hub. The staging module 204010 may include one or more programs to house the various types of data in its various native formats, including a staging database or universal file transfer protocols and repositories. This data then gets loaded into a data warehouse 204015, which may be configured to perform the analysis necessary to fuse or combine the data into common data types useful for people to analyze. The data warehouse module 204015 shows a few examples of the types of programs that may be used to perform these functions. Once the data is properly aligned, additional analysis can be performed on the composite data, which can then be utilized for providing business metadata (via the metadata layer 204020), reporting, and analysis (via the reporting and analysis layer 204025). The data sources from which data is extracted can include, for example, electronic medical records (EMR) databases and supply databases of product moving into the operating room (OR).

In one aspect, data can be loaded into the data warehouse 204015 via a number of different techniques. Known techniques for transferring data may be used, preserving the formats of the data. The speed at which the data may be transferred may be based more so on the means of transferring the data, such as through what physical means or if the data is transferred wirelessly. Utilizing hardware may be much faster than relying on software, as another example.

In one aspect, organized data can be loaded into a functional database for analysis. The process of data loading can depend on the structure of the data warehouse 204015. For example, metadata tends to be dramatically larger in size and significantly more ancillary to the primary data itself. Therefore, the data might be pooled/stored in one location so that it can be referenced faster/on the fly and the metadata could be stored in another location (e.g., offsite) and/or in a storage medium more appropriate for long term storage so that it is referenced when necessary or when directly asked for. Accordingly, the system can be configured to parse out the data and send it to the appropriate repositories. In such aspects, different datasets or data types could be manipulated in the absence of each other or the metadata could be used as a means to modify the primary data and then be put back in storage or otherwise removed from the combined dataset to limit the size of the dataset.

In another aspect, all the parts of the data (i.e., primary data and/or metadata) can be stored in predefined locations and a reference database can be configured to retrieve each of the pieces of data that are required by the current analysis, rather than having all the data stored in one cohesive database.

In one aspect, the data warehousing system 204015 can be configured to fuse dissimilar data, such as high and low-volume data. In one aspect, data that is received which is in a different format or structure than another dataset could use the metadata linked to the data point to allow the data to be fused into a format that is compatible with the other dataset. For example, data that is recorded at a vastly different data rate could be duplicated and placed in empty cells of a data storage structure. This technique can be used if, for example, the data source is a non-critical or supplementary data element or metadata to another critical data point. As another example, if the data rate is very high and it is being merged into a lower, more critical data form, the average of the data points or dropped data point methods could be used to provide a mean homogenous data flow. To illustrate, if kHz harmonic transducer data (i.e., transducer data that is sampled at a kHz rate) is being combined with or into outcomes-based 30 Hz data, the average of each 1,000 data points for blade impedance could be used with the lower sampling jaw clamp force to create a uniform time-based data stream. As another illustration, 3D imaging data could be transformed into a 2D planer version in the plane being measured by the adjacent mechanical device. In some aspects, the cloud system may help facilitate the determination of which data sets are more important than others, so as to determine how to effectively combine and align the data. Using situational awareness, the cloud system may recall from other datasets or various medical procedures which types of data are relied upon and most commonly adhered to. These datasets used by surgeons, analysts or others may provide probabilistic indications of what types of data are most useful, and then determine how to fuse the data for these purposes.

In various aspects, the data coming from one or more sources connected to a medical hub may be sent to the data warehouse and organized, scaled, and/or aligned using predefined parameters within the medical hub. That is, before integration or aggregation into the cloud system, the data may already be processed to fit a predefined format, scale, or other alignment when it is collected at the medical hub. In some cases, these predefined parameters may be adjusted after interaction with the cloud system. For example, the cloud system may determine that some data needs to be revised after including new medical devices into its system. As another example, the cloud system may utilize situational awareness or other machine learning to determine a more efficient scale of certain types of data that is more useful to an end user. These kinds of changes may be propagated to each of the medical hubs such that the automated data scaling, alignment, and organizing at the medical hub can provide more relevant data before being uploaded to the cloud system.

Data Cleansing

Data cleaning, also called data pre-processing, refers to the removal of duplicates, re-orientation of columns or rows, and linking of interrelated data.

In one aspect, the data warehousing system 204015 can be configured to remove duplicate data. Data duplication can result from the fact that data could be coming into the data warehouse system 204015 from multiple sources (see block 204005), several of which might be being used together during the course of a surgical procedure. For example, a robotic hub, an energy/visualization hub, and a monitor tower hub could all be interfacing within the same procedure, and each of these hubs may generate at least some data that overlaps, but is ultimately useful to have combined and aligned. Further, the hubs could be moved in and out of the OR for portions of a surgical procedure and even moved into other procedures. Being able to look for duplicate data sets coming from different sources and then being able to remove the duplicate data would keep specific users, usages, or regions from overly influencing conclusions drawn from the overall dataset resulting merely from duplication of the data. As another example, data might be duplicated due to an interruption or loss of data in transit, initiating a second transmission of the same data. As yet another example, data could be intentionally uploaded multiple times. All of these duplicates would affect the weighting of certain conclusions drawn from the datasets, which could interfere with trends and analysis.

In one aspect, the data warehousing system 204015 can be configured to merge separate streams of data. An alternative to multiple duplicates of data might occur when each of a series of devices or hubs that were used in the same procedure all send their data separately to the data warehouse 204015 assembling them. This presents an unrelated problem in that each device will require some fashion of synchronizing some continuous measure that allows the devices to be related with respect to one another. In aspects where the patient data is anonymized, then synchronization of the data from the different data sources can be even more challenging because a single synchronized real-time clock may not be an acceptable synchronizer (as storing real times associated with data could be used to ascertain confidential patient data). Further, if a randomized date and time are generated, then the randomizer would need to communicate that starting point to all devices to allow them to use the same time measure.

In one aspect, the surgical devices are configured to use the time of their internal clocks, rather than real-time, and communicate a synchronizer signal between the devices within the same procedure. Accordingly, each device records and time-stamps the data from their individualized points of view and then once all of the data is transmitted to a data warehouse (e.g., data warehouse 204015), the data warehouse could synchronize the signals and use that to interrelate or fuse the different data feeds into a signal unified dataset. This addresses the patient privacy issue while still successfully synchronizing the data.

Calculating Values From Independent Imported Data Elements

In one aspect, a portion of the metadata can be utilized to transform primary data points into related aspect data. For example, the data warehousing system 204015 could be configured to use tissue type to calculate a constant that is then multiplied with the tissue impedance to balance collagen level and conductivity with the impedance to create a comparable impedance value to evaluate seal strength that is comparable between tissue types. As another example, the data warehousing system 204015 could be configured to use tissue thickness and cartridge color for a surgical stapling instrument to calculate a constant that is then multiplied by force-to-fire (FTF) to create a tissue-independent value of device firing performance.

In one aspect, the generation of a particular surgical instrument or its serial number can be utilized to transform the instrument's behavior into a cascade that allows all devices to be compared across multiple design changes. The cloud system may propagate the change from one medical hub that is connected to the particular surgical instrument to all of the other medical hubs to the extent relevant. The new changes may also be incorporated into updating situational awareness for the medical device, noting that a new or updated version of the surgical instrument leads to a modification that should be taken into account.

Chronological Interrelated Data

In one aspect, chronological interrelated data can be stored as part of the patient's electronic health record (EHR) within HIPAA-controlled and protected privacy limits. The patient may then have access to a combined set of data derived from multiple different data sources. If for example multiple medical hubs and/or multiple medical devices were used in a surgery, the patient may be able to see how all of the instruments may have interacted in a chronological fashion, based on the fused and aligned data according to the processes described herein.

Randomized Data Pairs

In one aspect, non-trackable, seemingly unrelated data pairs or clusters can be integrated with outcomes. In one such aspect, the data wrangling process can include randomized data pairs and allowing the metadata resulting from the data to continue to be correlated to the outcomes which exist as part of the data pair or bundle.

Data Fit and Form Transformation

In various aspects, the fit and form of the data can be transformed so that the data is in an expected format (e.g., a format expected by the data warehousing system 204015). In one aspect, raw data can be mapped into particular functional forms at the data staging module 204010. For example, numeric data elements can be substituted for alphabetic data elements. In another aspect, data can be transformed into a predefined configuration, such as a particular arrangement of rows, columns, fields, cells, and so on.

Illustration 204100 in FIG. 17 shows a set of graphs including a first graph 204105 depicting measured blood pressure verse time, a second graph 204110 depicting fused blood pressure verse time, and a third graph 204115 depicting blood pressure verse time for different sample rates, in accordance with at least one aspect of the present disclosure. The graphs in illustration 204100 show examples of how some of the data coming into the data warehouse 204015 may contain different scales, sampling rates, and different measurements over time, and how the system of FIG. 16 may properly fuse and align the data to create a usable format. Here, graphs 204105 and 204110 are different data sets but shown on the same time scale. Graph 204105 represents a small set of measured blood pressure over time (over the period t₁). In this example, the line plot 204125 represents the actual blood pressure, while there are sampled data points shown mostly along the smooth, continuous line. The sampled data points actually contain a couple of error data points 204120. The data warehouse 204015, via processing by one or more medical hubs and/or the cloud system, may utilize techniques to account for the error points in order to form the correct blood pressure curve.

This measured blood pressure curve in graph 204105 may then may fused with other sampling data to create a fused blood pressure graph 204110. The time period is aligned as shown, along with additional data that may be gathered from other data sets. For example, the line plot 204140 may be a set of data from a slower sampling rate but that was recorded over the same time period. The blood pressure plot 204135 may be generated in part by the sampled data points in plot 204105, but also additional data. In the fused plot 204110, because the data warehouse 204015 would have processed the data to integrate it, error data points like points 204120 may be smoothed out. They may be removed and the revised plot 204135 may have in their places an average of the last data points before and after, in some cases. In other cases, simply the last data point may replace the error points if the rate of change of the data over unit time is greater than a predefined threshold.

The plot 204115 shows an example of low frequency data scaling. A downward sloping plot 204155 sampled at 100 Hz may be overlayed with data sampled at a lower frequency but then upscaled to be aligned with the higher sampling rate. Shown around the 100 Hz sampled plot 204155 are some data plots 204145 and 204150 that are sampled at 10 Hz but scaled to 100 Hz to match the higher sampled plot. Plot 204145 is an example of an error in the lower sampled plot but which is filled in and/or replacing the error points. As shown, the lower sampled points are scaled simply in a horizontal fashion at the higher frequency rate, in this example. In other cases, if enough data points are shown to establish a non-horizontal slope, the data warehouse 204015 may extrapolate the lower frequency sampling to create a more smooth fit, for example following the downward slope of plot 204155.

Illustration 204200 in FIG. 18 shows a graph 204205 depicting blood pressure relative to high and low thresholds, in accordance with at least one aspect of the present disclosure. The graph 204205 may be one example of a data analysis report that may utilize metadata resulting from the fusion and alignment of data from the data warehouse 204200. Graph 204205 may be a more finished product utilizing the data from FIG. 17. Thus, in this example, the plot of blood pressure 204210 may be the result of one or more sets of sampled data of a patient that may have been fused together, similar to one or more of the processes described above. Multiple data sets that were sampled at different data rates, e.g., between 2 Hz and 10 Hz, may have been used to create the plot 204210. Also, the system according to the present disclosure may add graphical overlays, such as the high limit line 204215 and low limit line 204220 to illustrate the range of blood pressure based on the data. Using the processes and systems described herein, a patient or analyst may be able to obtain more comprehensive data that has more beneficial use than if the multiple data sets were examined independently.

FIG. 19 is a graph depicting ultrasonic system frequency verse time, in accordance with at least one aspect of the present disclosure. Illustration 204300 may be another example of an end product report that is the result of data fusion and alignment by the data warehouse 204015 and subsequent analysis using metadata layer 204020 and/or reporting and analysis layer 204025. Here, graph 204305 shows a plot of ultrasonic frequency over time of a medical device. The plot 204315 may be the combined result of multiple data sets monitoring the device, with each data set having a sampling rate varying from 100 Hz to 2 kHz. The data sets may have been combined to produce this final plot 204315. As shown, the various sampling rates may have revealed that the frequency rate changed. The data sampled within the circle 204310 time period shows that the frequency of the device was decreasing somewhat gradually, while the data sampled within the circle 204320 time period shows that the frequency of the device decreased even more dramatically.

FIG. 20 is a graph depicting expected blood pressure for different vessel types, in accordance with at least one aspect of the present disclosure. Illustration 204400 may be another example of an end product report that is the result of data fusion and alignment by the data warehouse 204015 and subsequent analysis using metadata layer 204020 and/or reporting and analysis layer 204025. Here, graph 204405 shows how different types of blood vessels are either expected or reported to have different blood pressures. Each single data point in the graph 204405 may be the result of aggregate data compilation from multiple data samples. The data warehouse 204015 may have combined the data into a common scale and then organized it to be viewed like the plot as shown. Additional overlays may have been included, such as the labels and the vertical lines, to help better visualize the data.

As discussed above and illustrated in FIGS. 17-20, in various aspects a computer system can be configured to smooth or fuse data based on, for example, expected variations in the source data. For example, blood pressure as measured in the large arteries is not necessarily equivalent to the blood pressure measured in the smaller vessels (e.g., smaller arteries, arterioles, etc.) where an energy surgical instrument (i.e., an electrosurgical instrument or an ultrasonic surgical instrument) may be operating. Accordingly, the computer system can apply a scaling factor to the pressure measurement (i.e., the measured pressure in larger arteries) to put it in line with the pressure being experienced by the end effector of the device (i.e., the actual pressure in the smaller arteries being dissected, sealed, or otherwise manipulated by the surgical instrument end effector). Further, there can also be a lag in the blood pulse (pressure) measured in the artery and the correlated pressure to tissue property measured local to the end effector. Accordingly, the computer system can apply a time delay factor to shift the pressure measurement. Still further, different types of sampled data can have vastly different sampling rates and bit sizes. For example, ultrasonic feedback/control data may be sampled at, e.g., 100 Hz to 2 kHz (see FIG. 19), whereas blood pressure may be sampled at, e.g., 2 Hz to 0.25 Hz. Accordingly, the computer system can be configured to pair or fuse the data having the different sample rates using techniques discussed above in order to perform deep analysis on the data.

Dataset Validation

In various aspects, the computer system can be configured to validate the datasets themselves and/or the sources of the datasets, including the hubs, the individual instruments, or sensing systems. Further, a computer system (e.g., the surgical hub and/or cloud system) can be configured to validate received data and provide reactions to invalid data.

In one aspect, hub, instrument, and/or cloud can be configured to provide particular responses based on validation of a received dataset and authentication of its source and integrity. Further, the response(s) provided by the hub, instrument, and/or cloud could be selected from a set of reactions corresponding to the data and/or metadata. In one aspect, the cloud can be configured to isolate data from the primary data group in response to poor data integrity, a lack of data authenticity (i.e., the inability to authenticate the data or the ability to determine that the data is inauthentic), or user behavior. In another aspect, a computer system can be configured to provide a variety of responses, including identifying the user or the facility, isolating the data from other datasets, compiling the effects of the data to determine warehouse reactions, and/or providing warnings of inviolate instruments for procedures and their implications. In one aspect, the hub can be configured to provide a variety of responses, including flagging the data for later analysis, varying control algorithm changes of linked instruments, or preventing of usage of the hub or the instruments based on the validation or authenticity of the data, instruments, user behaviors, or linked data sources. In one aspect, a local user could have the ability to override the local Hub's reaction.

Data Trend Verification

In one aspect, the computer system can be configured to verify trends within the data to confirm that its behavior and therefore its data are unaltered. There could be several sources of error or invalid information that might move into large datasets. If all data is treated as valid, it could impact the statistical significance of other data points as it would create data that could move an average/correlation or increase the error term of the analysis to make insignificant something that was a significantly different event. Still further, data could be maliciously altered with the intent to hinder the ability to improve or detect something or cause the computer system to modify the devices' behaviors (e.g., control algorithms for the devices) in the wrong direction. Malicious intent could come from hiding the use when a device is being used off label, too many times (i.e., more times than recommended by the device manufacturer), or even in abusive manner. The intent could also be to damage the ability to effectively determine trends in the data or even to misdirect the data analysis.

There are sequential trends and repetitive data points that could be used throughout any normal surgical procedures that could not be fooled if the device was used for the jobs and when it was said to have been used. In one aspect, these comparison points could be used to verify the integrity of the data. This analysis of sequential trends and repetitive data points could not only be a check to verify a validation or encryption term, but it would also be as an on-the-fly means to assure that the data itself has not been affected in some way.

In one aspect, a validation term and/or private key encrypted checksum can be utilized to verify the data received is truly from the instrument it says it is from. For example, a validation term could be used as opposed to encrypting all data and metadata, which could be costly from a bandwidth, storage size, and processor speed point of view. Using a validation term could allow the data to be scanned in a less onerous manner via an encryption algorithm and key to allow the cloud and surgical hub units to verify the data is real and came from the purported specific source.

In one aspect, the cloud system may utilize data from other sources, such as one or more other medical hubs, to determine whether a dataset from a different medical hub is valid. The cloud system may be configured to draw from patterns of known valid datasets in multiple other medical hubs, and/or known invalid datasets from these multiple sources. In other cases, the cloud system may cross check data to determine whether the dataset is unique and whether that dataset should in fact be unique. For example, if data from a medical instrument that has a certain serial number happens to match a serial number from another known medical instrument, the dataset could be flagged.

In one aspect, if new malicious actors are discovered, the cloud system may utilize situational awareness to propagate the known instance of fraud or malicious activity to other hubs in the network. In general, situational awareness may be used to determine patterns of valid or invalid data and may apply those patterns to new situations or new nodes (e.g., hubs) in the network when determining the validity of any dataset.

In one aspect, if the data is found to be altered, the computer system can be configured to determine if the data is entirely contrived or has been modified.

If the data is determined to be entirely contrived or artificial, then the computer system can respond by notifying a security agent of the intrusion and to initiate an investigation into the data source and behavior; quarantining all data and data requests from the affected hubs, regions, or system users; and/or preventing erroneous data from being added to any of the databases (e.g., a data warehousing database) or from affecting or being considered as part of any analyses.

If the data is determined to be altered (e.g., in order to affect data correlation analyses), but is determined to be from a valid source, then the computer system can respond by flagging the data and identifying the data source as a source of contaminated data. An example of this would be for a knock-off device that knows it is being monitored to generate slightly off data with the intent to hide the fact that it is not as effective as the original devices. Another example would be for a repossessed device to contain a mathematical constant that is used to offset the aging calibration of the original device that has been affected by its uses (i.e., overuse) or re-sterilization. These datasets could be verified by the instrument being instructed to operate in a given way during a controlled situation. For example, an instrument can be programmed to close the jaws at first start up, activate the transducer with a known power level, and then review the blade harmonics. As another example, a powered stapler can be programmed to retract the firing member when the knife is in its fully retracted state and monitor the force measured by the system. By being programmed to operate in a particular known manner in a controlled situation, the instrument can thus determine whether its operation is being altered or otherwise affected.

If the data is determined to be invalid and it has characteristics of invalid data that the system has seen before, the data could be used to determine the source and purpose of the invalidation. The data could then be relayed back to the hub from which it came to inform the users that they are being affected by products or individuals that are interfering with the proper operation and best outcomes of their devices.

Layered Contextual Information

In some aspects, contextual information can be layered onto data to enable contextual transformation, rather than merely aggregation, of datasets. In other words, contextual metadata can be linked to the outcome and device data to enable contextual transformation of the datasets.

In one aspect, a system (e.g., a surgical hub system, a cloud analytics system, and so on) can be programmed to adjust devices' control programs based on stratified contextual data, in addition to the data. The contextual data represents the circumstances around the collection of the data or related patient, procedure, surgeon, or facility information. The stratified analysis for determining interrelationships of influencing factors can be utilized to create an improved causational response for the surgical hub and instrument control program updates. In one aspect, the stratification of context includes a hierarchy of influencing factors, where some can be more important or functionally interrelated at a higher priority than other interrelations. In one aspect, the data pairs include the outcome of the instrument operation and its functional parameters. In one aspect, the contextual parameters are derived from the patient complications, other treatments, co-morbidities, procedure complications, previous instrument functional parameters, and so on. In one aspect, the adjustments based on these contextual limitations or influencing factors can proportionately affect the adjustments.

Identification of Relevant Contextual Cues

FIG. 21 is a block diagram 204500 depicting layered contextual information, in accordance with at least one aspect of the present disclosure. In this illustration, there are four examples of types of layered contextual information that may be accounted for by the systems of the present disclosure. In general, the cloud system, in connection with one or more medical hubs, may be configured to adjust connected devices based on one or more sets of contextual data. FIG. 21 provides some examples of how to determine what adjustments to make when there are multiple contextual datasets, and the datasets come into conflict with one another or otherwise need to be reconciled with one another. In the example A 204505, the present disclosure allows for a hierarchical conflict resolution system of tiered contextual information for when at least some of the contextual information conflicts. For example, a first set of primary contextual information relevant to a medical device may include managing the speed of a medical device that leads to an instruction to not exceed an initial activation rate of 10 mm/sec. However, a second set of primary contextual information may be entered that relates to diseased tissue that is being operated on, and leads to an instruction to not exceed an initial activation rate of 8 mm/sec. The hierarchical conflict resolution scheme 24505 may include logic to create a combined set of instructions that satisfies all combined constraints. In this case, the resolved primary set of contextual information therefore leads to an instruction to have the initial activation rate of the medical instrument not exceed 8 mm/sec. In other cases where instructions directly contradict one another, contextual information at a higher tier in the hierarchy may take precedence. In cases where there are contradicting instructions in the same tier, a flag may be presented that highlights the irresolvable conflict. In other cases, situational awareness may be utilized to refer back to past instances of such conflict to help determine how to resolve.

In the example B 204510, adjustments to device control parameters may be made after resolving conflicts between different tiers in a non-standard manner. For example, a primary dataset of contextual information may contain a max force to fire of 400 lbs, while a secondary dataset of contextual information related to what type of medication a patient is taking may indicate a max force of only 150 lbs. A tertiary dataset of contextual information may have additional instructions for max force to fire based on patient parameters. In this case, the secondary contextual information may override the parameters of the primary contextual information because the patient possesses a high BMI, or there is some other overriding constraint. In some cases, the primary contextual dataset may include one or more exceptions to defer to different parameters, if they exist in other lower tiered datasets. In this case, the primary dataset may provide an exception to use different max force to fire if patient medication requires it, and thus the secondary contextual dataset will override this condition for this case.

In the example C 204515, adjustments of control parameters may be determined by combining multiple pieces of contextual information, rather than simply overriding one over another. As an example, a primary set of contextual information may lead to an instruction to set the initial activation rate of the instrument to 8 mm/sec, while a tertiary set of contextual information about patient parameters may lead to an instruction to decrease speed by an additional 20% due to a diseased tissue state. In this case, the effects do not directly contradict, but rather they may be combined to create a revised instruction. Here, the speed set to 8 mm/sec is a decrease from the default 10 mm/sec, which is an initial decrease of 2 mm/sec. The tertiary instruction leads to an additional decrease of 20%, or 2.0*0.2=0.4 mm/sec. Therefore, the final reduced speed is 8−0.4=7.6 mm/sec. The cloud system may be configured to interpret the logic of the instructions and generate the adjusted device parameters based on the combination of contextual information.

In the example D 204520, secondary or tertiary effects can still be used to override any predefined control parameters that a primary contextual dataset does not speak to. Generally, the secondary and tertiary contextual datasets may be based on patient specific parameters, and therefore lead to changes made at the time of surgery or “on the fly.” In some cases, new contextual information may be provided in real time, which may then cause additional adjustments to the device(s).

FIG. 22 is a block diagram 204600 depicting instrument functional settings, in accordance with at least one aspect of the present disclosure. Illustration 204600 of FIG. 22 provides an example of how multiple sets of contextual information may apply to the same device simultaneously, but are applied conditionally at different times or in different functions occurring at the same time. For example, different settings may apply to the firing system of an instrument and separately to the clamping system of the instrument. How the control settings are applied according to any of the examples of FIG. 21 may be applied in different instrument contexts as shown in FIG. 22. Therefore, multiple sets of contextual information may simultaneous apply to a single instrument. In some cases, this may result in some lower tiered effects being applied to the instrument during a particular function, while those same lower tiered effects would not be applied to the instrument during a different function.

FIG. 23 is a graph 204700 depicting force to fire (FTF) and firing velocity for patients having different complication risks. Illustration 204700 shows four plots, with two plots 204705 and 204720 corresponding to the vertical axis on the left pertaining to firing velocity, and two plots 204710 and 204715 corresponding to the vertical axis on the right pertaining to force to fire. The two plots corresponding to one type of axis correspond to two different patients and how an instrument's settings might vary between two patients with different health conditions. In various aspects, the contextual information adjustments to a single instrument may be queued into the same instrument, to account for different patients that the instrument may be applied to over the course of a day. In various aspects, the instrument may be configured to load in a particular file to disease contextual information, instrument contextual information, treatment contextual information, patient contextual information, and so on, where the combined contextual information from the multiple different datasets may form a particular combination that provides the optimal adjustments to the instrument for a particular surgery on a particular patient. For example, for the first patient, contextual information about emphysema stage 3 may be loaded into the instrument for the disease state. The force to fire information for the particular instrument may be loaded for the instrument state. Radiation treatment contextual information may be loaded for the treatment state, and steroid dosage contextual information may be loaded for the patient state. Any conflicts or combinations may be resolved using the example processes described in FIGS. 21 and 22, which then provides the instrument with a particular set of adjustments for precisely handling this first patient.

Next, a second set of contextual information for a dealing with a second patient may also be loaded into the instrument but remained queued up before being implemented. For example, for the second patient, contextual information about high blood pressure (e.g., 165/110) may be loaded into the instrument for the disease state. The force to close information for the particular instrument may be loaded for the instrument state. Chemotherapy contextual information may be loaded for the treatment state, and blood thinner dosage contextual information may be loaded for the patient state. Any conflicts or combinations may be resolved using the example processes described in FIGS. 21 and 22, which then provides the instrument with a particular set of adjustments for precisely handling this second patient.

The resulting combinations of contextual information for the first patient may result in the two graphs 204705 and 204710, for example, for the firing velocity and the force to fire settings over time, respectively. Similarly, the resulting combinations of contextual information for the second patient may result in the two graphs 204720 and 204715, for example, for the firing velocity and the force to fire settings over time, respectively.

In one aspect, the adjustments to the instrument for a single setting may be weighted by the hierarchical tier in which the proposed adjustment derives from. For example, if adjustments to FTF are found in all of the primary, secondary, and tertiary tiers, then the adjustment to FTF may be made according to the following example weighting structure: FTF=FTF(default)+1.5*FTF(primary)+1.0*FTF(secondary)+0.75*FTF(tertiary), wherein FTF is force required to fire a surgical stapler.

Other weighting mechanisms may be used as well and are non-limiting.

Discussed below are some non limiting examples of contextual information that may be included to cause adjustments to an instrument. The type and number of factors described may be used in the processes described in FIGS. 21, 22, and 23 to create a combined or comprehensive instrument adjustment. The contextual cues can include non-device-specific cues, device-specific cues, medical cues, patient-specific cues, procedure-specific cues, and surgeon-specific cues.

Non-Device-Specific Contextual Cues

Non-device-specific cues are contextual cues related to the operation of a device, but that are not specific to any particular type of device. Non-device-specific contextual cues can include, for example, device tissue clamping, tissue information, and instrument usage history.

Tissue clamping contextual cues can include, for example, implications of clamp force or pressure on tissue (i.e., the primary effect(s) of the tissue clamping), which can in turn include desired and adverse impacts on the tissue. Clamping of tissue can have multiple different desired effects on the clamped tissue. For example, clamping the tissue can drive the fluids out of the tissue, collapse the tissue layers, and collapse any interior opening(s). This allows the tissue layers to be in close proximity and prevents leaks from any hollow structures in the area (e.g., capillaries, bronchi, gastro-intestinal). Another desired effect of tissue clamping is that because body tissues are viscoelastic, the compressibility of the tissue is dependent on the type of tissue, its fluid content, the pressure level, and the rate of compression. Accordingly, for the same amount of compression, the faster the compression, the higher the applied force. For a constant pressure, the tissue will continue to move thinner and thinner until a stable state of full compression is achieved. This continued thinning is defined as tissue creep and is a function of the viscoelasticity of tissue. This is important in the discussion or pre-compression cycles, wait times, and overall instantaneous compression. Overall lower compression levels over a longer period of time are less detrimental to the treatment tissue and the pressure differential (shear) on the adjacent tissue.

Clamping the tissue can also have adverse impacts on the tissue due to compression. For example, as the tissue is clamped and tissue structures are collapsed, there may be structures in the tissue that are not intended to be collapsed. This can create a micro tissue tension or pressure differential between the adjacent unclamped tissue and the compressed tissue. Some tissues (e.g., parenchyma, solid organ parenchyma) are not particularly tolerant to such tension or pressure without causing ruptures of the tissue layers adjacent to the clamped tissue, which in turn causes inadvertent collateral tissue damage and, potentially, additional leaks. The total amount of pressure, the geometry of the clamping bodies, and the rate of clamping all have primary effects on the likelihood of collateral damage. Furthermore, the tissue composition, strength, and internal parameters also have an implication of the likelihood of damage. Many of these internal parameters of the tissue are influenced by other medical treatments, disease states, or physiologic states of the tissue. All of these can complicate the likelihood of collateral or primary site secondary damage. As another example, the maximum compression levels for different tissues and organ are at different levels. Most tissues in the body are a series of layers or structures enclosed within other structures. Once maximum compression occurs when the outer enclosing layer has too much pressure or pressure differential applied, it tears, allowing the internal constrained tissue out. The lung is a good example of this. The lung parenchyma is made up of alveoli, veins, arteries, and bronchi with an exterior visceral and parietal pleura covering the surface. When stapling lung parenchyma, it is desirable to staple the pleura to itself to promote good healing. But a tear in the pleura can expose the more fragile alveoli and without an outer constraining element, the alveoli easily rupture, creating air leaks. Another form of maximum tissue compression occurs when the fiber bundles of the tissue itself are ruptured or separated. This occurs at a much higher level and this tissue destruction is typically accompanied by wide spread cellular death and necrosis.

A number of different device control parameters influence clamping, such as the clamping force, the clamping rate, and the number of repetitive clamps. The clamping force characteristics can be illustrated by a clamp force vs. time curve, which can indicate the time rate of change of force, peak clamping force, time to clamp force stabilization, steady state clamping force, and difference between peak clamp force and stabilized clamping force.

Clamping force can be measured either directly or indirectly via a proxy. A number of different proxies can be utilized to measure the clamping force. For powered closure, the proxies can include the current through the motor and the difference between the target motor speed and the actual motor speed. Strain gauges on components that are loaded during the act of clamping, such as the anvil, the closure member, and/or the support frame can also be utilized to measure the clamping force.

Clamping rate can be determined by comparing the actual clamping rate against the targeted clamping rate for powered closure. Clamping rate can also be determined according to the duration of the clamping process from start to finish.

The number of repetitive clamps can be important because heavy tissue manipulation prior to transection of tissue treatment can have a cumulative effect on the tissue due to the repetitive exposure of pressure to the tissue. Some devices have a maximum pressure that can be applied and also a minimum closure level for the next mechanism to begin its operation. In these instances, the jaws are opened and closed repeatedly to get the tissue to the minimum closed state while compressing over and over until that state is achieved. In one aspect, a robotic surgical system can signal if clamping parameters do not fall within a threshold to ensure the jaws are properly clamped.

Tissue information contextual cues can include, for example, placement in jaw (which can be considered, e.g., a secondary effect from the surgical procedure), tissue quality knowledge from other sources (e.g., imaging or EMR, which can indicate prior interventions, current/prior diseases, and so on), type, thickness or density, and impedance (which can be considered, e.g., a primary effect from the surgical procedure). The placement of the tissue within the jaws can correspond to the percentage of the jaws covered by the tissue, the region or locations of jaws covered by tissue (e.g., vessel, etc.), and the degree of bunching or degree of uniformity of the tissue along the length of the jaws. Any device that compresses tissue is applying a known measurable force to the tissue within the jaws. The amount of tissue in the jaws, the placement of the tissue (i.e., relative distal to proximal position), and its thickness variability impact the pressure on the tissue. Knowing the force applied to the tissue without knowing how much of the jaws are covered by the tissue or the placement of the tissue makes it challenging to determine the pressure on the tissue. Many devices are also technique sensitive. For example, often only the distal tip of an ultrasonic device is used for dissection, welding, and cutting, leaving the bulk of the jaw empty for many of the firings. As another example, surgical stapling and cutting instruments often have the tissue crammed into the proximal crotch of the jaws, leaving a differential of tissue from the proximal to distal end of the end effector. Unless the only meaningful information that is sought is the trends of the parameters (which can be the case in certain situations), adjustments to the device control parameters are functionally dependent on knowing how much of the jaw is loaded and where the jaws is loaded because those factors have implications of the forces measured.

Instrument usage history contextual cues can include, for example, the number of uses and the number of resterilizations of the devices.

Device-Specific Contextual Cues

There are a wide variety of device-specific contextual cues for staplers, ultrasonic instruments, laparoscopic or endoscopic suturing devices, dual bipolar instruments, monopolar instruments, and clip appliers.

Contextual cues for stapling devices can include device and reload identification cues and firing speed cues (which can be considered, e.g., a primary effect from the surgical procedure).

Device and reload identification cues can include, for example, the stapler type and reload (i.e., cartridge) information. Stapler type cues can include the brand, powered verse manual, shaft length, general purpose verse specialty, handheld verse robotic, single verse multiple use, usage history, whether the device has been reprocessed (e.g., authentic reprocessing or resterilized, off-label usage), and stapler configuration (e.g., linear, curved linear, or circular). Reload cues can include the color, length, uniform verse variable staple height, authentication (i.e., whether the reload is compatible and of the same brand; whether the reload is compatible, but of a different or unknown brand device or an illegal knock-off reload used with the manufacturer's stapler; not compatible; or of the correct technology generation, such as a), specialty (e.g., curved tip, reinforced, radial, absorbable staples, medicament-coated staples, or tissue thickness compensation), use and type of buttress or other staple line adjunct, or provides medicament delivery via staple line adjunct.

Firing speed cues can include, for example, actual speed verse time throughout the firing cycle, the difference between the target speed and the actual speed, or adaptive firing control of firing speed (e.g., starting speed, the number of changes in target speed, and the maximum and minimum actual speeds recorded). Firing speed has multiple direct and/or indirect implications on device function. For example, firing speed can have implications of staple formation for multiple reasons.

As one reason, the rate at which an I-beam or bladed actuator is cycled causes tissue to move while the staples are being deployed. In a circular stapler, the knife advancement is often coupled to the staple driver advancement. If the knife begins to sever the tissue before the staples are fully formed (as may be the case), the tissue begins to move radially outward. This tissue flow can have implications on the staple formation. In sequentially linear deploying staplers, the knife/actuator progresses through the cartridge (typically proximal to distal, although sometimes it can be in the opposite direction) and severs tissue while progressively forming staples, which creates tissue flow in the direction of the movement. Pre-compression and tissue stabilization features can reduce this effect, while lower tissue compressions and !-beam local running compressions typically increase this effect. The tissue movement effect can create a wave in advance of the cutting member, which occurs in a related area to where the unformed staples are being advanced towards the anvil. Accordingly, this tissue flow has an effect on staple formation and cut line length.

As another reason, the rate at which the staple drivers are advanced has an effect on their advancement and the likelihood of them rotating or bending, causing the crown of the staple to move out of plane. For surgical stapling and cutting instruments where the diameter of the device is constrained to the trocar diameter, size, or type, the staple drivers are often short and have an aspect ratio that allows for driver roll, in addition to linear advancement perpendicular to the tissue contacting surface of the cartridge. This driver roll can result in a bind in the advancement of the driver so that as the sled continues to advance, the drivers are rotated, rather than advancing outwardly, resulting in the destruction of the cartridge and the staple line. This roll of the staple drivers is a function of sliding friction, lubrication, and cartridge geometry. The binding and therefore the driver rolling is amplified by the rate at which the sled is advanced and, therefore, the rate of the firing actuator of the device. Furthermore, many staplers overdrive the drivers above the tissue contacting surface of the cartridge. This overdrive exposes the driver to tissue flows occurring between the anvil and the cartridge. The drivers have a moment of inertia and are being driven up into contact with the tissue, as well as experiencing the loads from the staples being formed. The rate at which these drivers are advanced into the tissue and the extent of the overdrive both influence the likelihood of the driver remaining directly under the anvil forming pocket.

As another example, firing speed can have implications on local tissue compression. In the case of I-beam coupled surgical stapling and cutting instruments, the advancement of the firing member causes local tissue compression around the I-beam location, in addition to cutting tissue and deploying the staples against the anvil pockets. This local running compression wave moves proximal to distal with the I-beam location. The viscoelastic aspect of the tissue causes the rate of this advancement to be directly related to the local compression force applied, as well as the size of the rolling compression wave. This local rolling compression is capable of causing local tissue tension damage within the treatment area, as well as collateral damage because the rate of compression is likely more than the rate of pre-compression.

As yet another example, firing speed can have implications regarding forces within the device. The loads experienced within the instrument itself are a cumulative effect from the pre-compression, as well as the local rolling compression. The faster the firing member is advanced, the higher the required force to advance the firing member. This is due to the dynamics of the moving structures within the device as well as the I-beam forces. The higher these forces are, the more stretch there is in all the components in the elongated tube and frame, which in turn results in some forces being impacted (e.g., pre-compression deteriorate as the system stretches). These losses then add more force to the firing member balance, resulting in even higher impacts on the firing speed to load relationship.

These various examples of stapler contextual cues can be further illustrated with regards to a specific example. In this example, through the procedure plan, EMR, and other hospital records, several things are known: (i) this is a right upper lobectomy procedure; (ii) the patient has had prior radiation to treat the tumor in this area; and (iii) the patient has interstitial lung disease. These pieces of information suggest that the lung will be stiffer than normal, healthy tissue. Based on this inference, the conservative approach would be to slow down the maximum rate of closure and adjust the thresholds. However, further layered contextual information can be further utilized in determining how the instrument should be controlled. On this same patient, during closure, the force to close is higher than anticipated, exceeding the new thresholds (i.e., the thresholds that we set according to the procedure and patient information noted above). As a result, the wait time prior to starting the firing sequence is increased and the initial firing speed is slowed. Firing algorithms will take over once firing has been initiated. Note that the contextual cues can influence thresholds within the firing algorithms.

Contextual cues for ultrasonic devices can include the activation time, the coherence tomography evaluation of the collagen content of the tissue, the current blend of energy modalities (e.g., whether the instrument utilizes an ultrasonic/bipolar blend, an ultrasonic/monopolar blend, or ultrasonic only), blade temperature, and pad condition. Blade temperature increases with the duration of contact with either tissue or the clamp arm pad and the power into the transducer. This temperature changes the natural frequency of the blade and has the ability to add heat into the welding of tissue that is not intended. It can also cause inadvertent damage to tissue that it comes into contact with, even in-between actuations of the instrument. Hot blades also cause local tissue charring (which can then stick to the blade). The blade temperature has a long-term effect on cut/coagulation performance, but also creates a shear or tearing force on the tissue weld as the jaws are unclamped and removed (as charred tissue sticks to the blade). The pad condition can depend upon the duration of time active without tissue in the jaws and/or the temperature history of the jaws. As the pad is degraded, the underlying metallic strength of the clamp arm is exposed to the blade, eventually impacting its performance.

These various examples of ultrasonic contextual cues can be further illustrated with regards to a specific example. In this example, through the procedure plan, EMR, and other hospital records, several things are known: (i) this is a vertical sleeve gastrectomy procedure; (ii) the patient's BMI 40; and (iii) the patient's body composition suggest that they have a high level of visceral fat. These pieces of information suggest that the greater curvature takedown will have a higher than normal volume of fatty mesentery. Based on this inference, the blended algorithm leans more heavily toward cutting than sealing given the high expected fat content. Algorithm parameters can be concurrently adjusted to ensure a robust seal.

Contextual cues for laparoscopic or endoscopic suturing devices can include stitch tension (tension monitoring can be utilized to inform the suturing technique, for example), stitch type (e.g., mattress verse running, etc.), or suture type (e.g., braided verse monofilament, absorbable verse non-absorbable, suture diameter/size, or needle size/type). Pattern recognition models can be utilized to recognize the pattern of the stitches placed verse tension applied in applying the stitches (e.g., stitch/tension/stitch/tension verse stitch/stitch/tension/stitch/stitch/tension). The pattern recognition system can be configured to provide technique advisements, e.g., three stitches without a tension step with a braided suture may be difficult to cinch up without tissue damage, whereas two stitches may be suitable.'

Contextual cues for dual jaw bipolar RF instruments can include the coating (which can include coatings disclosed in U.S. Pat. Nos. 5,693,052 and 5,843,080, which are each hereby incorporated by reference in their entirety), design (which can include the design disclosed in U.S. Pat. No. D399,561, which is hereby incorporated by reference in its entirety), bipolar coagulation, algorithms and load curves, smoke generated, conductivity contact of electrodes (e.g., the amount of charring present on electrodes or whether there is a detected short), jaw compression (e.g., the compressive force, pressure, or in the special case of bipolar shears the localized electrode cross-section/elevated geometry or higher max force, as is disclosed in U.S. Pat. No. 9,084,606, which is hereby incorporated by reference in its entirety), tissue gap (i.e., whether the jaw is open/feathering or closed/spacing between jaws), electrode configuration (e.g., opposed electrodes, offset electrodes, or electrodes/insulation, as disclosed in U.S. Pat. Nos. 5,100,402, 5,496,315, 5,531,743, 5,846,237, and/or 6,090,107, each of which is hereby incorporated by reference in its entirety).

Contextual cues for monopolar instruments can include power (e.g., constant voltage or current and variable control of the other given a tissue impendence), tissue impendence (e.g., rate of change of impendence, overall measured impendence, or time at a given impendence), algorithms and load curves, return path capacity, blade technology (e.g., the coating of blade, such as insulation breakdown or various coatings described in U.S. Pat. Nos. 5,197,962, 5,697,926, 5,893,849, 6,685,704, 6,783,525, 6,951,559, each of which is hereby incorporated by reference herein in its entirety; geometry of exposed conductive surfaces; conductivity of structural underling materials; or blade configurations, such as the configurations described in U.S. Pat. Nos. 6,039,735, 6,066,137, 8,439,910, each of which is hereby incorporated by reference in its entirety), heat dissipation, smoke generated, applied compression (e.g., compression force between the mono-polar blade and the support arm or driving force of a monopolar probe pressed against tissue), or leakage current magnitude.

Contextual cues for clip applier devices can include clip size, first verse last clip from the applier, the timing of the detected forces (e.g., overload protection mechanisms if unexpectedly stiff structures are inadvertent within jaws, such as when clipping over another clip or closing jaws on another instrument), clip feeding monitoring (e.g., feeding loads or detection of the presence of clips in pre-defined locations or at pre-defined times), lateral end effector and bending loads, or jaw actuation load (e.g., clip closure, max load in displacement controlled actuation, max displacement in load controlled actuation, or tissue manipulation loads with jaws).

Medical Contextual Cues

Medical contextual cues can include contextual cues associated with medical complications, disease states, medications, and procedure complexity.

Contextual cues for medical complications can include functional constipation, functional diarrhea, sphincter control or strength insufficiency disorders, functional dyspepsia, and complications that effect tissue planes & tissue compositional makeup, as just some examples.

Functional constipation can result from colorectal surgery including a circular anastomosis, which would suggest a longer period before bowel motility after surgery to allow for more expansion. It could also suggest the use of an expandable staple configuration or counter indicate the use of buttress or compression ring technologies that would not tolerate larger more solid feces (which can be, e.g., a secondary effect from the surgical procedure as a tissue fragility complicating effect).

Functional diarrhea can indicate higher acid levels and more fluidics movements, which could dictate tighter staple forms, higher pre-compressions, and the potential need for abdomen-side applied secondary adjuncts to minimize the likelihood of fecal introduction into the abdomen cavity (which can be defined as a tertiary effect from the surgical procedure).

Poor sphincter control or other strength in sufficiency disorders can result in heartburn or acid reflux. These contextual cues can indicate, for example, that an esophageal anastomosis would benefit from stronger staples, tighter forms, and/or longer precompression of the tissue to enable tighter staple forms and lower tissue tension thresholds (both micro and macro tension). Macro tissue tension is related to more extensive mobilization of the tissue from adjacent structures and could me measured by lateral forces on the stapling device. Micro tissue tension is due to the compression rate, max compression, and the gradient of tissue compression between the areas directly adjacent to the treatment area and the type of staple forms within the treatment area. 3D staples lower micro tension as does a lower max pre-compression level. Heartburn and/or acid reflux can be considered, e.g., a secondary effect of the surgical procedure. Device or surgery suggestions could include a reinforcement treatment for the sphincter or an adjunct therapy applied to the staple lines to improve robustness.

Functional dyspepsia can result from the sensation or inhibition of peristalsis, which could suggest rigidity of the anastomosis line (would further amplify the effect). Lower micro tissue tension would ease the effect (3D staples) or an expandable staple line. Compression of anastomosis lines or adjunct material used on the staple lines would cause more issues. Functional dyspepsia can be considered, e.g., a secondary effect as an adjunct therapy usage complication effect.

In the case of complications that effect tissue planes and tissue compositional makeup, healing from a first surgery and adhesions result in an increase in thickness of the tissue as well as disorganized remodeling of the tissue, resulting in increased toughness of the tissue. To adjust for these effects when stapling, it would be suggested to implement an increased gap, raised tissue load thresholds, slower actuation, and suggested larger or heavier staples. These complications can be considered, e.g., a secondary effect of the surgical procedure as a tissue thickness/toughness complication effect. Other such complications can include revisional surgery, adhesions, and altered tissue conditions from medical treatments (e.g., irradiated tissue or steroid induced changes).

Contextual cues for disease states can vary for colorectal, thoracic, metabolic, and cardiovascular diseases.

For colorectal diseases, inflammatory bowel disease can be a contextual cue. All of the repetitive inflammatory colorectal diseases cause an increase in thickness of the tissue, as well as disorganized remodeling of the tissue, resulting in increased toughness of the tissue. The stapling adjustments could include increased gap, raised tissue load thresholds, slower actuation, and suggested larger or heavier staples. These complications can be considered, e.g., a secondary effect of the surgical procedure as a stapling adjunct complication effect. Such colorectal diseases can include Crohn's disease and diverticulitis.

For thoracic diseases, bronchitis, emphysema, chronic obstructive pulmonary disease (COPD), asthma and interstitial lung disease can all be contextual cues. For example, emphysema results in thicker, stiffer lung tissue that would suggest load clamping rates, lower pre-compression levels, and slower firing actuation of the knife/I-beam to prevent adjacent collateral damage around the perimeter of the anvil and cartridge due to excessive pressure differential during treatment. These are primary effects. As another example, COPD results in artery walls that could be stiffer and less elastic, requiring a softer handling of the arteries prior to applying treatment. This could require the mechanical clamping elements to clamp at a slower rate and potentially a lower clamp pressure to minimize damage outside of the treatment area and premature damage to the treatment area. These adjustments could be for either energy or stapling. These are secondary effects. In stapling, the suggestion of an adjunct many be counter indicated to prevent additional uncontrolled remodeling and hardening. In advanced energy, the RF treatment modality is preferred and the balance of the RF to ultrasonic balance to weld more than cut.

For metabolic diseases, metabolic syndrome, obesity, and type 2 diabetes mellitus can all be contextual cues.

For cardiovascular diseases, arteriosclerosis, high cholesterol, and vascular disease can all be contextual cues.

Contextual cues for medications can include blood thinning, blood clotting, steroids, radiation treatment, and chemotherapy.

For example, blood thinning can indicate that advanced energy devices would benefit from improved coagulation before cutting. This is a priority and primary effect as a blood pressure complication effect. In a hybrid energy device, the RF power could be increased or the ultrasonic application delayed in order to increase the coagulation before the cutting. In an ultrasonic-only device, the algorithm could be adjusted to apply a lower powered level for a longer period of time to in order to denature the collagen longer before cutting. Further, in an ultrasonic-only device the harmonic power could be adjusted lower as the temperature approaches a predefined optimum temperature and that temperature could be maintained for a longer period of time before elevating the power of the transducer to initiate cutting. Further, in a stapling instrument, the suggested cartridge color could be adjusted down, suggesting shorter formed staples or the closure clamping time increased or the pressure increased before firing. This is a secondary effect.

For example, blood clotting can indicate that precompression or the compression levels of either the advanced energy or stapling could be lowered prior to treatment to minimize inadvertent damage and therefore forming clotting outside of the treatment area. This is a secondary effect as an increased pressure complication effect.

For example, steroids cause physiologic effects that slow healing and raise blood pressure, which increases pre-existing complications. This is a tertiary effect as an amplification of disease complication effects, as well as longer term healing complications. Steroids raise blood pressure in many people who take it. One reason is that steroids and other corticosteroids cause the body to retain fluid. Extra fluid in the circulation can cause an increase in blood pressure. Further, anti-inflammatory corticosteroids significantly impair wound healing. Corticosteroids lower transforming growth factor-β (TGF-β) and insulin-like growth factor-1 (IGF-I) levels and tissue deposition in wounds and that retinoids stimulate corticosteroid-impaired TGF-β and IGF-I release and collagen production.

For example, radiation treatment can result in inflammation of the organ and thickening of the tissue wall. This effect can increase the stiffness, thickness, and toughness of the tissue being treated. This increases the need for longer compression times and potentially higher compression thresholds, unless complications inhibit that. Radiation treatments can also have complication effects on blood oxygenation thru impacting the respiration system and can have a multiplicative effect on collagen vascular disease which could in turn require changes in any advanced energy welding energy blending or algorithms leaning towards more time to weld at a slower rate before cutting. This is a secondary effect as a tissue composition makeup complication effect.

For example, chemotherapy treatment can result in the tissue becoming thin and friable. These effects make collateral damage to the tissues much more likely and more difficult to treat. The implications for any mechanical device is lower manipulation forces and precompression levels, as well as lower rate thresholds and in general more gentle handling and tissue tensions needed. This is a primary effect as complication effects with higher tissue compressions.

Contextual cues associated with procedure complexity include the location of a tumor, remaining vascularization, the challenge of accessing the surgical site, the total time under anesthesia, the amount of work required to complete the procedure, and whether there were any prior procedures.

For example, the remaining vascularization can be a contextual cue because vascularization is directly related to the rate of healing and tissue viability. Further, it has longer-term implication on tissue strength and recovery. This is a primary physiologic impact on healing. This does not have any short-term instrument operation implications, but does have implications on recovery strength and reinforcement, needing additional time of the primary surgical treatment durability. This may impact the instrument's recommendations for post-surgery recovery, additional adjunct therapies applied, and required monitoring. This is a secondary effect as an amplification of disease state, blood sugar level, and oxygenation impacts on tissue remodeling.

For example, the total time under anesthesia can be a contextual cue because the time under anesthesia is a complicating effect on recovery to pre-surgery levels relating to oxygenation levels and metabolic reactions. During the surgery, it has an amplification effect on lower blood oxygenation levels. This is a time-dependent effect that is not linear; the longer the time, the higher the impact of the effects become. This is a tertiary effect as a complication effect on lower blood oxygenation levels.

For example, the amount of work required to complete the procedure can be a contextual cue because it relates to the number of cycles of energy, the number of dissector or scissors moves, and/or the number of surgical stapling instrument firings.

For example, prior procedures can be a contextual cue because prior procedures increase the likelihood of adhesions and secondary remodeling of tissues. This typically creates more disorganized tissue planes and tougher tissues with more covering tissues. This is a tertiary effect as amplification of disease complication effects, as well as collagen level complication effects.

Patient-Specific Contextual Cues

Patient-specific contextual cues can include, for example, patient parameters and physiologic cues.

Patient parameters can include age, gender, whether the patient is a smoker, BMI, and body composition information.

For example, age results in friable tissues that would require a lower compression and lower rate of compression of the treatment devices, especially in the pre-treatment compressions. This is a secondary effect as a higher tissue compression complication effect.

For example, gender has threshold implication shifts for the ideal ranges of many physiologically related measures (e.g., BMI, body fat composition, and age impacts on physiology). This is a tertiary effect on other parameters.

For example, whether the patient is a smoker results in thicker, stiffer lung tissue that would suggest load clamping rates, lower pre-compression levels, and slower firing actuation of the knife/I-beam to prevent adjacent collateral damage around the perimeter of the anvil and cartridge due to excessive pressure differential during treatment. Theses are secondary effects as emphysema and oxygen saturation complication effects. Tissue oxygenation can be, as noted below, a metric available to quantify effect of smoking.

For example, BMI is a contextual cue because obesity tends to increase co-morbidities of many other medical complications. This is a tertiary effect as an amplification complication effect with blood sugar levels, congestive heart issues, oxygenation levels, and several other disease states.

For example, body composition information can be contextual cues because body fat percentage affects the collagen content of tissues, the compressive properties of the tissue types, and metabolic implications on healing & tissue remodeling. This has effects at both too high and too low of a percentage with differing effects at each extreme. Body fat percentages over a given level inhibit metabolic operation and add to complications around organ function. These complications will tend to amplify disease state complications on the mechanical device functions. Further, body fat percentages below a given level will tend to have impact on the tissue makeup itself. These tissue makeup changes can have impacts both on healing as well as advanced energy devices ability to weld consistently due to fluctuations in collagen levels, requiring more compression and longer weld times. These are tertiary effects as amplification of disease complication effects, as well as collagen level complication effects.

Physiologic cues can include the time since the patient last ate, fasting blood glucose level, blood pressure, macro tissue tension, tissue fluid levels, and tissue oxygenation.

For example, fasting blood glucose level can be a contextual cue because blood sugar level is the main physiologic factor in healing. When blood sugar level is higher than normal, it prevents nutrients and oxygen from energizing cells and prevents your immune system from functioning efficiently. This is a secondary effect. Further, knowing the steady-state fasting state, as well as the post-meal changes and reactions, impact the implications of a measured value. In most humans this varies from about 82 mg/dl to 110 mg/dl (4.4 to 6.1 mmol/l). The blood sugar levels rises to nearly 140 mg/dl (7.8 mmol/l) or a bit more in normal humans after a full meal. In humans normal blood glucose levels are around 90 mg/dl, equivalent to 5 mM (mmol/l)

This measure is also time dependent. Consuming carbohydrate heavy food would cause a dramatic increase in blood sugar, but it would typically also begin to decrease after around 30 minutes.

For example, blood pressure can be utilized as a contextual cue because advanced energy devices benefit from improved coagulation before cutting. This is a priority and primary effect. In a hybrid energy device, the RF power could be increased or the ultrasonic application delayed in order to increase the coagulation before the cutting. In an ultrasonic-only device, the algorithm could be adjusted to apply a lower powered level for a longer period of time to in order to denature the collagen longer before cutting. Further, in an ultrasonic-only device the harmonic power could be adjusted lower as the temperature approaches a predefined optimum temperature and that temperature could be maintained for a longer period of time before elevating the power of the transducer to initiate cutting. Blood pressure can be measured using different methods and at different locations. For example, the 10 minute resting pressure, i.e., resting blood pressure, can be very different than any blood pressure resulting from exertion. Knowing if this is a blood pressure measure based on an acute measure or is considered a systemic resting pressure will have implications on how to respond to the measure and its exceeding of upper or lower pressures. Further, differences exist for vascular levels of blood pressure. Typical blood pressure is taken in larger arteries within an arm or other extremity. A pressure in arteries in the arm of 129/80 could relate to a micro pressure of 70/40 in the capillaries and even lower 20/10 in veins where the actual tissue treatments are being preformed. Occlusions and variations in physiology can amplify or constrain the differences in pressure from one part of the system to another. Knowing where the pressure is being taken and any long-term measures could help adjust the effects needed due to changes in pressure.

For example, tissue fluid levels can be a contextual cue because dehydration reduces blood flow throughout the body, while also consequently lowering blood pressure, it can starve the wound bed of white blood cells that protect against infection, while also limiting oxygen reaching the wound site by way of blood flow, as do vitamins and nutrients. In general, lower fluid levels inhibit every aspect of wound healing. This is a tertiary effect as an amplification of healing complication effects. For surface tissue remodeling and potentially colorectal site healing, dehydration can delay healing in several ways. A warm, damp environment is ideal for the growth of new tissue, and a lack of moisture to the affected area can halt cellular development and migration. Without proper moisture, the epithelial cells that migrate across the wound bed to repair tissue along the way cannot properly navigate and cover the wound site. This interrupts the creation of new tissue and leaves the wound open and susceptible to harmful bacteria that can cause infection. Potential measures as related to dehydration can include, for example, electrolytes, blood urea nitrogen, creatine, urinalysis, complete blood count, and urine and/or blood osmolality.

For example, tissue oxygenation can be a contextual cue because tissue oxygenation is widely recognized to play a role in nearly every part of the wound healing stages. When healing, a surgical site develops an increased need for bacterial defense, cell proliferation, collagen synthesis and angiogenesis, among other reparative functions. Collagen accumulation is a direct function of oxygen tension and levels below 20 mmHg have been shown to impair accumulation. Collagen synthesis is dependent on functions of enzymes that are in turn a function of local oxygen levels. By contrast, hyperbaric oxygen therapy has been shown to increase healing rates by increasing the oxygen concentrations above normal. While the tumor tissue is metabolically designed to thrive under conditions of hypoxia, hypoxia of the wound primarily caused by vascular limitations is intensified by coincident conditions (e.g., infection, pain, anxiety and hyperthermia) and leads to poor healing outcomes. This is a tertiary effect as an amplification of healing complication effects. Tissue oxygenation can be measured according to oxygen delivery (DO2), oxygen uptake/consumption (VO2), oxygen tension (PO2), or hemoglobin oxygen saturation (SO2). Further, several other techniques are available for measuring tissue oxygenation, such as near infrared spectroscopy (NIR).

Procedure-Specific Contextual Cues

Procedure-specific contextual cues can include the time of day the procedure occurred, whether it was an emergency versus a planned surgery, the length of the procedure, the type of procedure (e.g., laparoscopic, robotic, or open), and whether it was a reoperative or original procedure.

Surgeon-Specific Contextual Cues

Surgeon-specific contextual cues can include whether the surgeon was a specialist or a general practitioner (this comparison made against the procedure to be performed), the skill level of the surgeon (which can be indicated by, e.g., the total number of procedures performed, total number of times performed the current operation, and/or training level), and the focus or energy of the surgeon (which can be indicated by, e.g., the number of other procedures performed that day and the duration of current procedure).

Management of Metadata and Data

In various aspects, the metadata (e.g., the contextual cues described above) can be included with the general data generation.

In one aspect, the metadata can be stored by attaching the metadata to the primary data with the ability to filter the data out from the metadata. In another aspect, the metadata can be stored in a location other than the primary data, but can be linked to, allowing for reaching into the metadata for key metadata.

The accessibility of metadata can be controlled in a variety of different manners. In one aspect, the linked metadata can be transported with the original collected data. In another aspect, data can be extracted from the metadata by filtering data and relevant context.

Knowledge Hierarchy

In various aspects, contextual cues can be organized to provide data based on the needed context. Accordingly, the computer system can be configured to identify or determine the relevance of specific metadata to provide context. Further, a computer system can be programmed to provide navigation thru metadata.

Utilization Methodology

In one aspect, the metadata can be utilized for (i) identification and linking of isolated but interrelated data points or records, (ii) identification of linked occurrences, and/or (iii) algorithms can be programmed to automatically compare outcomes (and complaints) to any/all recorded data and compare regression trends and model capability of prediction to determine which factors can influence success. This data can be limited to a single device (e.g., speed of firing or energy verse leak) or can be combined between multiple devices to infer such things as time of firing relative to placement of trocars or start of anesthesia (beginning of surgery) or number of activations of scissors/dissectors/energy devices (e.g., degree of dissection/removal of fat).

Various aspects of the subject matter described herein are set out in the following numbered examples:

Example 1—A system for automatically fusing data from a medical procedure. The system comprises a medical hub comprising at least one processor and at least one memory, and a remote server communicatively coupled to the medical hub. The at least one processor is configured to access a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period, access a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period, scale the second dataset to match the first data sampling rate, fuse the first dataset and the second dataset into a composite dataset, align the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded, cause display of the composite dataset, generate a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset, and transmit the composite dataset to the remote server. Example 2—The system of Example 1, wherein the first or the second dataset comprises one or more error data points, and wherein the processor is further configured to smooth out the one or more error data points. Example 3—The system of Examples 1 or 2, wherein the graphical overlay comprises a horizontal axis and a vertical axis, and wherein the composite dataset is displayed in a graph form according to the horizontal and vertical axes. Example 4—The system of Example 3, wherein the graphical overlay further comprises visual boundaries that indicate visual limits of the composite dataset. Example 5—The system of Examples 1 or 4, wherein the graphical overlay comprises a horizontal axis, a first vertical axis and a second vertical axis, wherein the first dataset comprises data related to a first measurement that is expressed by the first vertical axis over the horizontal axis, and wherein the second dataset comprises data related to a second measurement different than the first measurement that is expressed by the second vertical axis over the horizontal axis. Example 6—The system of any one of Examples 1-5, wherein the processor is further configured to access first metadata associated with the first dataset and recorded during the sampling time period, access second metadata associated with the second dataset and recorded during the sampling time period, transmit the first and second metadata to an offsite repository, and store the first and second datasets in the memory of the system. Example 7—The system of any one of Examples 1-6, wherein the first dataset is recorded in a first format, wherein the second dataset is recorded in a second format different from the first format, and wherein the processor is further configured to convert the first and second datasets into a common format. Example 8—The system of any one of Examples 1-7, wherein the processor is further configured to determine duplicate data from the first and the second datasets and remove all copies of the duplicate data before fusing the first and the second datasets into the composite dataset. Example 9—The system of any one of Examples 1-8, wherein the first dataset is generated by a first device having a first internal clock, the second dataset is generated by a second device having a second internal clock, and the first dataset and the second dataset do not have a common time period due to the first and the second datasets being recorded by their respective internal clocks. The processor is further configured to access a synchronizer signal between the first and second device and align the first dataset and the second dataset using the synchronizer signal to interrelate the first dataset and the second dataset. Example 10—The system of any one of Examples 1-9, wherein the processor is further configured to access first metadata associated with the first dataset and recorded during the sampling time period, access second metadata associated with the second dataset and recorded during the sampling time period, transform the first dataset into first related aspect data using the first metadata, and transform the second dataset into second related aspect data using the second metadata, wherein fusing the first dataset and the second dataset into the composite dataset comprises fusing the first related aspect data with the second related aspect data. Example 11—The system of any one of Examples 1-10, wherein the remote server is configured to access updated parameters from one or more other medical hubs communicatively coupled to the remote server and propagate the updated parameters to the medical hub, wherein the medical hub is configured to adjust the composite dataset according to the updated parameters. Example 12—A method of a system for automatically fusing data from a medical procedure. the system comprising a medical hub comprising at least one processor and at least one memory. The method comprises accessing a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period, accessing a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period, scaling the second dataset to match the first data sampling rate, fusing the first dataset and the second dataset into a composite dataset, aligning the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded, causing display of the composite dataset, generating a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset, and transmitting the composite dataset to a remote server. Example 13—The method of Example 12, wherein the first or the second dataset comprises one or more error data points, and wherein the method further comprises smoothing out the error data points. Example 14—The method of Examples 12 or 13, wherein the graphical overlay comprises a horizontal axis, a first vertical axis and a second vertical axis, wherein the first dataset comprises data related to a first measurement that is expressed by the first vertical axis over the horizontal axis, and wherein the second dataset comprises data related to a second measurement different than the first measurement that is expressed by the second vertical axis over the horizontal axis. Example 15—The method of any one of Examples 12-14, wherein the first dataset is recorded in a first format, wherein the second dataset is recorded in a second format different from the first format, and wherein the method further comprises converting the first and second datasets into a common format. Example 16—The method of any one of Examples 12-15, further comprising determining duplicate data from the first and the second datasets and removing all copies of the duplicate data before fusing the first and the second datasets into the composite dataset. Example 17—The method of any one of Examples 12-16, wherein the first dataset is generated by a first device having a first internal clock, the second dataset is generated by a second device having a second internal clock, and the first dataset and the second dataset do not have a common time period due to the first and the second datasets being recorded by their respective internal clocks. The method further comprises accessing a synchronizer signal between the first and second device and aligning the first dataset and the second dataset using the synchronizer signal to interrelate the first dataset and the second dataset. Example 18—The method of any one of Examples 12-17, further comprising accessing first metadata associated with the first dataset and recorded during the sampling time period, accessing second metadata associated with the second dataset and recorded during the sampling time period, transforming the first dataset into first related aspect data using the first metadata, and transforming the second dataset into second related aspect data using the second metadata, wherein fusing the first dataset and the second dataset into the composite dataset comprises fusing the first related aspect data with the second related aspect data. Example 19—The method of any one of Examples 12-18, further comprising accessing, by the remote server, updated parameters from one or more other medical hubs communicatively coupled to the remote server, propagating, by the remote server, the updated parameters to the medical hub, and adjusting, by the medical hub, the composite dataset according to the updated parameters. Example 20—A computer readable medium comprising no transitory signals and comprising instructions that, when executed by a processor, cause the processor to perform operations. The operations comprise accessing a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period, accessing a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period, scaling the second dataset to match the first data sampling rate, fusing the first dataset and the second dataset into a composite dataset, aligning the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded, causing display of the composite dataset, generating a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset, and transmitting the composite dataset to a remote server.

While several forms have been illustrated and described, it is not the intention of Applicant to restrict or limit the scope of the appended claims to such detail. Numerous modifications, variations, changes, substitutions, combinations, and equivalents to those forms may be implemented and will occur to those skilled in the art without departing from the scope of the present disclosure. Moreover, the structure of each element associated with the described forms can be alternatively described as a means for providing the function performed by the element. Also, where materials are disclosed for certain components, other materials may be used. It is therefore to be understood that the foregoing description and the appended claims are intended to cover all such modifications, combinations, and variations as falling within the scope of the disclosed forms. The appended claims are intended to cover all such modifications, variations, changes, substitutions, modifications, and equivalents.

The foregoing detailed description has set forth various forms of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, and/or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. Those skilled in the art will recognize that some aspects of the forms disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as one or more program products in a variety of forms, and that an illustrative form of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution.

Instructions used to program logic to perform various disclosed aspects can be stored within a memory in the system, such as dynamic random access memory (DRAM), cache, flash memory, or other storage. Furthermore, the instructions can be distributed via a network or by way of other computer readable media. Thus a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), but is not limited to, floppy diskettes, optical disks, compact disc, read-only memory (CD-ROMs), and magneto-optical disks, read-only memory (ROMs), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or a tangible, machine-readable storage used in the transmission of information over the Internet via electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Accordingly, the non-transitory computer-readable medium includes any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

As used in any aspect herein, the term “control circuit” may refer to, for example, hardwired circuitry, programmable circuitry (e.g., a computer processor including one or more individual instruction processing cores, processing unit, processor, microcontroller, microcontroller unit, controller, digital signal processor (DSP), programmable logic device (PLD), programmable logic array (PLA), or field programmable gate array (FPGA)), state machine circuitry, firmware that stores instructions executed by programmable circuitry, and any combination thereof. The control circuit may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), an application-specific integrated circuit (ASIC), a system on-chip (SoC), desktop computers, laptop computers, tablet computers, servers, smart phones, etc. Accordingly, as used herein “control circuit” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.

As used in any aspect herein, the term “logic” may refer to an app, software, firmware and/or circuitry configured to perform any of the aforementioned operations. Software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on non-transitory computer readable storage medium. Firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in memory devices.

As used in any aspect herein, the terms “component,” “system,” “module” and the like can refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution.

As used in any aspect herein, an “algorithm” refers to a self-consistent sequence of steps leading to a desired result, where a “step” refers to a manipulation of physical quantities and/or logic states which may, though need not necessarily, take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It is common usage to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. These and similar terms may be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities and/or states.

A network may include a packet switched network. The communication devices may be capable of communicating with each other using a selected packet switched network communications protocol. One example communications protocol may include an Ethernet communications protocol which may be capable permitting communication using a Transmission Control Protocol/Internet Protocol (TCP/IP). The Ethernet protocol may comply or be compatible with the Ethernet standard published by the Institute of Electrical and Electronics Engineers (IEEE) titled “IEEE 802.3 Standard”, published in December, 2008 and/or later versions of this standard. Alternatively or additionally, the communication devices may be capable of communicating with each other using an X.25 communications protocol. The X.25 communications protocol may comply or be compatible with a standard promulgated by the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T). Alternatively or additionally, the communication devices may be capable of communicating with each other using a frame relay communications protocol. The frame relay communications protocol may comply or be compatible with a standard promulgated by Consultative Committee for International Telegraph and Telephone (CCITT) and/or the American National Standards Institute (ANSI). Alternatively or additionally, the transceivers may be capable of communicating with each other using an Asynchronous Transfer Mode (ATM) communications protocol. The ATM communications protocol may comply or be compatible with an ATM standard published by the ATM Forum titled “ATM-MPLS Network Interworking 2.0” published August 2001, and/or later versions of this standard. Of course, different and/or after-developed connection-oriented network communication protocols are equally contemplated herein.

Unless specifically stated otherwise as apparent from the foregoing disclosure, it is appreciated that, throughout the foregoing disclosure, discussions using terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

One or more components may be referred to herein as “configured to,” “configurable to,” “operable/operative to,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Those skilled in the art will recognize that “configured to” can generally encompass active-state components and/or inactive-state components and/or standby-state components, unless context requires otherwise.

The terms “proximal” and “distal” are used herein with reference to a clinician manipulating the handle portion of the surgical instrument. The term “proximal” refers to the portion closest to the clinician and the term “distal” refers to the portion located away from the clinician. It will be further appreciated that, for convenience and clarity, spatial terms such as “vertical”, “horizontal”, “up”, and “down” may be used herein with respect to the drawings. However, surgical instruments are used in many orientations and positions, and these terms are not intended to be limiting and/or absolute.

Those skilled in the art will recognize that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to claims containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that typically a disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms unless context dictates otherwise. For example, the phrase “A or B” will be typically understood to include the possibilities of “A” or “B” or “A and B.”

With respect to the appended claims, those skilled in the art will appreciate that recited operations therein may generally be performed in any order. Also, although various operational flow diagrams are presented in a sequence(s), it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently. Examples of such alternate orderings may include overlapping, interleaved, interrupted, reordered, incremental, preparatory, supplemental, simultaneous, reverse, or other variant orderings, unless context dictates otherwise. Furthermore, terms like “responsive to,” “related to,” or other past-tense adjectives are generally not intended to exclude such variants, unless context dictates otherwise.

It is worthy to note that any reference to “one aspect,” “an aspect,” “an exemplification,” “one exemplification,” and the like means that a particular feature, structure, or characteristic described in connection with the aspect is included in at least one aspect. Thus, appearances of the phrases “in one aspect,” “in an aspect,” “in an exemplification,” and “in one exemplification” in various places throughout the specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more aspects.

Any patent application, patent, non-patent publication, or other disclosure material referred to in this specification and/or listed in any Application Data Sheet is incorporated by reference herein, to the extent that the incorporated materials is not inconsistent herewith. As such, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions, statements, or other disclosure material set forth herein will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.

In summary, numerous benefits have been described which result from employing the concepts described herein. The foregoing description of the one or more forms has been presented for purposes of illustration and description. It is not intended to be exhaustive or limiting to the precise form disclosed. Modifications or variations are possible in light of the above teachings. The one or more forms were chosen and described in order to illustrate principles and practical application to thereby enable one of ordinary skill in the art to utilize the various forms and with various modifications as are suited to the particular use contemplated. It is intended that the claims submitted herewith define the overall scope. 

What is claimed is:
 1. A system for automatically fusing data from a medical procedure, the system comprising: a first medical hub comprising at least one processor and at least one memory, and a remote server communicatively coupled to the first medical hub, wherein the at least one processor is configured to: access a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period; analyze the first dataset to determine that the first dataset has not been altered, by comparing the first dataset to valid medical data from a second medical hub and determining that a pattern within the first dataset is consistent with the valid medical data from the second medical hub; access a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period; analyze the second dataset to determine that the second dataset has not been altered, by comparing the second dataset to second valid medical data from a third medical hub and determining that a pattern within the second dataset is consistent with the second valid medical data from the third medical hub; scale the second dataset to match the first data sampling rate; fuse the first dataset and the second dataset into a composite dataset; align the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded; cause display of the composite dataset; generate a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset; and transmit the composite dataset to the remote server.
 2. The system of claim 1, wherein the first or the second dataset comprises one or more error data points, and wherein the at least one processor is further configured to smooth out the one or more error data points.
 3. The system of claim 1, wherein the graphical overlay comprises a horizontal axis and a vertical axis, and wherein the composite dataset is displayed in a graph form according to the horizontal and vertical axes.
 4. The system of claim 3, wherein the graphical overlay further comprises visual boundaries that indicate visual limits of the composite dataset.
 5. The system of claim 1, wherein the graphical overlay comprises a horizontal axis, a first vertical axis and a second vertical axis, wherein the first dataset comprises data related to a first measurement that is expressed by the first vertical axis over the horizontal axis, and wherein the second dataset comprises data related to a second measurement different than the first measurement that is expressed by the second vertical axis over the horizontal axis.
 6. The system of claim 1, wherein the at least one processor is further configured to: access first metadata associated with the first dataset and recorded during the sampling time period; access second metadata associated with the second dataset and recorded during the sampling time period; transmit the first and second metadata to an offsite repository; and store the first and second datasets in the at least one memory of the system.
 7. The system of claim 1, wherein the first dataset is recorded in a first format, wherein the second dataset is recorded in a second format different from the first format, and wherein the at least one processor is further configured to convert the first and second datasets into a common format.
 8. The system of claim 1, wherein the at least one processor is further configured to: determine duplicate data from the first and the second datasets; and remove all copies of the duplicate data before fusing the first and the second datasets into the composite dataset.
 9. The system of claim 1, wherein: the first dataset is generated by a first device having a first internal clock; the second dataset is generated by a second device having a second internal clock; and the first dataset and the second dataset do not have a common time period due to the first and the second datasets being recorded by their respective internal clocks; wherein the at least one processor is further configured to: access a synchronizer signal between the first and second device; and align the first dataset and the second dataset using the synchronizer signal to interrelate the first dataset and the second dataset.
 10. The system of claim 1, wherein the at least one processor is further configured to: access first metadata associated with the first dataset and recorded during the sampling time period; access second metadata associated with the second dataset and recorded during the sampling time period; transform the first dataset into first related aspect data using the first metadata; and transform the second dataset into second related aspect data using the second metadata; wherein fusing the first dataset and the second dataset into the composite dataset comprises fusing the first related aspect data with the second related aspect data.
 11. The system of claim 1, wherein the remote server is configured to: access updated parameters from one or more other medical hubs communicatively coupled to the remote server; and propagate the updated parameters to the one or more other medical hubs; wherein the one or more other medical hubs is configured to adjust the composite dataset according to the updated parameters.
 12. A method of a system for automatically fusing data from a medical procedure, the system comprising a first medical hub comprising at least one processor and at least one memory, the method comprising: accessing a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period; analyzing the first dataset to determine that the first dataset has not been altered, by comparing the first dataset to valid medical data from a second medical hub and determining that a pattern within the first dataset is consistent with the valid medical data from the second medical hub; accessing a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period; analyzing the second dataset to determine that the second dataset has not been altered, by comparing the second dataset to second valid medical data from a third medical hub and determining that a pattern within the second dataset is consistent with the second valid medical data from the third medical hub; scaling the second dataset to match the first data sampling rate; fusing the first dataset and the second dataset into a composite dataset; aligning the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded; causing display of the composite dataset; generating a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset; and transmitting the composite dataset to a remote server.
 13. The method of claim 12, wherein the first or the second dataset comprises one or more error data points, and wherein the method further comprises smoothing out the error data points.
 14. The method of claim 12, wherein the graphical overlay comprises a horizontal axis, a first vertical axis and a second vertical axis, wherein the first dataset comprises data related to a first measurement that is expressed by the first vertical axis over the horizontal axis, and wherein the second dataset comprises data related to a second measurement different than the first measurement that is expressed by the second vertical axis over the horizontal axis.
 15. The method of claim 12, wherein the first dataset is recorded in a first format, wherein the second dataset is recorded in a second format different from the first format, and wherein the method further comprises converting the first and second datasets into a common format.
 16. The method of claim 12, further comprising: determining duplicate data from the first and the second datasets; and removing all copies of the duplicate data before fusing the first and the second datasets into the composite dataset.
 17. The method of claim 12, wherein: the first dataset is generated by a first device having a first internal clock; the second dataset is generated by a second device having a second internal clock; and the first dataset and the second dataset do not have a common time period due to the first and the second datasets being recorded by their respective internal clocks; wherein the method further comprises: accessing a synchronizer signal between the first and second device; and aligning the first dataset and the second dataset using the synchronizer signal to interrelate the first dataset and the second dataset.
 18. The method of claim 12, further comprising: accessing first metadata associated with the first dataset and recorded during the sampling time period; accessing second metadata associated with the second dataset and recorded during the sampling time period; transforming the first dataset into first related aspect data using the first metadata; and transforming the second dataset into second related aspect data using the second metadata; wherein fusing the first dataset and the second dataset into the composite dataset comprises fusing the first related aspect data with the second related aspect data.
 19. The method of claim 12, further comprising: accessing, by the remote server, updated parameters from one or more other medical hubs communicatively coupled to the remote server; propagating, by the remote server, the updated parameters to the one or more other medical hubs; and adjusting, by the one or more other medical hubs, the composite dataset according to the updated parameters.
 20. A computer readable medium comprising no transitory signals and comprising instructions that, when executed by a processor, cause the processor to perform operations comprising: accessing a first dataset comprising data sampled at a first data sampling rate recorded during a sampling time period from a first medical hub; analyzing the first dataset to determine that the first dataset has not been altered, by comparing the first dataset to valid medical data from a second medical hub and determining that a pattern within the first dataset is consistent with the valid medical data from the second medical hub; accessing a second dataset comprising data sampled at a second data sampling rate that is slower than the first data sampling rate and is recorded during the sampling time period; analyzing the second dataset to determine that the second dataset has not been altered, by comparing the second dataset to second valid medical data from a third medical hub and determining that a pattern within the second dataset is consistent with the second valid medical data from the third medical hub; scaling the second dataset to match the first data sampling rate; fusing the first dataset and the second dataset into a composite dataset; aligning the first dataset and the second dataset in the composite dataset, such that data from both the first dataset and the second dataset is sequentially ordered in the composite dataset in an order in which the data was recorded; causing display of the composite dataset; generating a graphical overlay on top of the display of the composite dataset that provides an interpretation of the composite dataset; and transmitting the composite dataset to a remote server. 